Результатом
предыдущего пункта стал выбор наиболее
привлекательного с инвестиционной
точки зрения проекта. Следовательно,
нужно детально проанализировать все
возможные исходы этого проекта.
Среди
методов оценки финансовых рисков
инвестиционных проектов одним из
наиболее распространенных является
метод сценариев. Он предполагает
формирование оптимистического,
пессимистического и наиболее вероятного
сценариев развития событий. В качестве
последнего примем условия, при которых
получено значение показателя «Чистая
приведенная стоимость» по оптимальной
последовательности и обозначим его
«NPVн.в.».
Оптимистический
и пессимистический варианты отличаются
от наиболее вероятного представлениями
проектанта о конечном результате.
Очевидно, что развитие событий в
оптимистическом сценарии приведут к
тому, что NPV
изменится в большую сторону, а в
пессимистическом – в меньшую. Чтобы в
этом убедиться, необходимо описать оба
варианта развития событий, конкретизировать,
какие именно изменения произойдут в
денежных потоках (статьях затрат или
выручке) и произвести расчет показателя
NPVн.в.
проекта
при пессимистических и оптимистических
прогнозах развития событий.
В
итоге получим NPVопт
(оптимистическое),
NPVпесс
(пессимистическое),
NPVн.в.
(наиболее
вероятное). Большое значение для оценки
риска имеет также вероятность Р, с
которой могут развиваться события по
каждому из перечисленных сценариев.
Эту величину необходимо оценить
самостоятельно и занести в табл.3.
Таблица
3
Вероятности
развития события по пессимистическому,
оптимистическому и наиболее
вероятному сценариям
|
Показатели |
Пессимистический |
Наиболее |
Оптимистический |
|
Рi |
|||
|
NPVi |
Наиболее
общим статистическим показателем оценки
риска в методе сценариев является
среднеквадратичное отклонение (СКВО).
Для его нахождения необходимо определить
математическое ожидание
:
(3)
где
n
– количество сценариев.
Среднеквадратическое
отклонение СКВО
считается
следующим образом:
(4)
Полученное
значение СКВО характеризует меру
рассеивания, т.е. степень возможных
вариаций в ходе реализации проекта. В
зависимости от этой величины необходимо
сделать вывод о рискованности данного
проекта. При этом нужно исходить из
того, что риск – величина субъективная,
т.е. зависимая от лица, который оценивает
рискованность проекта. А, как известно,
склонность к риску при прочих равных
условиях у разных лиц различна.
Раздел 3. Определение показателей финансовой эффективности по проекту строительства судна
3.1. Определение структуры себестоимости по статьям затрат и структуры выручки по видам производства
Первичной
исходной информацией для определения
затрат, связанных с постройкой судна
является себестоимость судна, уровень
рентабельности и коэффициент, отражающий
отношение между производственной
себестоимостью без налогов и тем же
показателем, включающим налоги. Исходная
информация указана в Задании на курсовой
проект.
Надо
отметить, что себестоимость, обозначенная
в Задании на курсовой проект, является
производственной
себестоимостью,
включающей затраты по всем статьям
расходов только на производство. Для
целей данного курсового проекта также
различают производственную себестоимость,
которая включает налоги и ряд
внепроизводственных (коммерческих)
расходов, т.е. расходов, связанных с
продажей продукции, передачей прав
собственности (трансакционные издержки)
и др. В данной работе будем считать
отношение между двумя видами себестоимости
равным 1,5, хотя надо понимать, что данный
коэффициент у различных предприятий
по разным видам продукции может
значительно отличаться. Заданное
отношение между производственной
себестоимостью без налогов и
производственной себестоимостью,
включающей налоги, необходимо использовать
в расчетах для нахождения цены судна:
Цена
судна
= производственная себестоимость х
норма рентабельности х 1,5
Подробная
классификация затрат на строительство
судна приведена в курсе «Экономика
предприятия». Для целей настоящего
курсового проекта необходимо рассмотреть
структуру себестоимости по видам
производства. Условно считаем, что
себестоимость распределяется
пропорционально трудоемкости, структура
которой для разных видов судов приведена
в табл. 4 (столбец а).
В столбце в
таблицы приведена структура распределения
платежей, сумма которых составляет цену
судна; это распределение следует принять
за основу при определении поступлений
(платежей) за выполнение части работ по
судну (платежи по частичной готовности).
Правда, необходимо отметить, что 100% в
итоговой строчке – это цена судна без
учета аванса от заказчика; в таблице
приведено распределение стоимости
судна по видам производства, оставшейся
после оплаты авансового платежа.
Например, если аванс составил 10% от цены,
то по видам производства распределяются
оставшиеся 90%.
Таблица 4
Объемы трудоемкости (а) и платежей по
частичной готовности (в) для различных
типов судов в %% (величины условные)
|
№ п/п |
Вид |
Виды |
|||||
|
Танкер |
Сухогруз |
Траулер |
|||||
|
а |
в |
а |
в |
а |
в |
||
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
|
1 |
КОП |
16 |
18 |
8 |
9 |
8 |
9 |
|
2 |
СС |
30 |
34 |
24 |
24 |
26 |
26 |
|
3 |
СТП |
28 |
13 |
24 |
15 |
22 |
13 |
|
4 |
ТМ |
5 |
8 |
9 |
12 |
8 |
5 |
|
5 |
ММ |
5 |
5 |
10 |
11 |
16 |
18 |
|
6 |
ДСТР |
7 |
10 |
13 |
17 |
6 |
18 |
|
7 |
СДТ |
6 |
9 |
7 |
8 |
6 |
7 |
|
8 |
МШСТР |
3 |
3 |
5 |
4 |
8 |
4 |
|
9 |
ИТОГО |
100 |
100 |
100 |
100 |
100 |
100 |
Величину
производственной себестоимости по
каждому виду производства можно разбить
на три укрупненные группы:
—
материалы;
—
заработная плата основных производственных
рабочих;
—
прочие расходы: накладные расходы,
амортизация, проценты за кредит.
Практика
судостроения показывает, что наибольший
удельный вес в себестоимости занимают
материалы – 50–60%, заработная плата ОПР
– 15–20%, соответственно прочие расходы
– 20–35%. Структура
затрат для каждого вида производства
различна, но в курсовой работе соотношение
элементов затрат 50:15:35 будет сохранено
для всех видов производства.
Необходимо
также учесть и сумму первоначальных
инвестиций, величина которых указана
в Задании на курсовой проект. Данные
затраты, направленные в основном на
капитальное строительство и воспроизводство
основных фондов, должны отображаться
во временной точке «0», которая
предшествует проектному циклу.
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Пессимистический сценарий
Cтраница 1
Пессимистический сценарий исходит из худших вариантов работы, оптимистический основан на учете наилучших вариантов исхода, реалистический ( базовый) строится на средних показателях изменения уровня определяющих факторов.
[1]
Но существуют и пессимистические сценарии. Уменьшение населения развитых стран открывает Эльдорадо перед странами большого демографического взрыва. Народы, находящиеся в неблагоприятных условиях, но на подъеме роста населения, могут присвоить себе — добром или силой — земли и ресурсы народов богатых, но находящихся на спаде. Эти последние постепенно будут смешиваться с пришельцами, пока не потеряют свою индивидуальность. Они исчезнут, как исчезли уже многие народы, попав в подобную ситуацию [ Нас было 80 миллиардов.
[2]
Вместе с тем возможен и пессимистический сценарий развития событий.
[3]
Рассмотрим, например, результаты анализа пессимистического сценария. Что касается увеличения капитальных вложений, то эксперты оценят вероятность этого события очень высоко и порекомендуют инвестору отнестись с должным вниманием к такой возможности.
[4]
Анализируя данные табл. 8, можно сделать следующий вывод: пессимистические сценарии не дают отрицательного или близкого к нулю ЧДД; их ЧДД и другие результативные показатели вполне приемлемы для инвестора, если учитывать небольшую вероятность их реализации.
[5]
В основе рекомендаций лежит правило: даже в оптимистическом варианте нельзя оставить проект для дальнейшего рассмотрения, если значение NPV такого проекта отрицательно, и наоборот: пессимистический сценарий в случае получения положительного значения NPV позволяет эксперту судить о приемлемости данного сценария проекта, несмотря на наихудшие прогнозы изменения факторов.
[6]
Автор в полном объеме раскрыл роль риска в хозяйственной деятельности человека, всесторонне охарактеризовал все имеющиеся в арсенале теории вероятностей методы прогнозирования будущего с целью принятия эффективного решения, выбор которого минимизирует возможность пессимистического сценария.
[7]
На рис. 4.13 — рис. 4.15 приведены графики, показывающие три сценария развития предприятия при проведении расчетов предприятия с проектом и без проекта: текущий, пессимистический и оптимистический. В пессимистическом сценарии предполагается, что цены на продукты предприятия будут на 10 % меньше, а затраты на сырье и материалы на 15 % больше. В оптимистическом сценарии предполагается, что цены на продукты предприятия будут на 10 % больше, а затраты на сырье и материалы на 15 % меньше.
[8]
Окно постоянно находится на экране и позволяет, находясь в таблице или графике, выбрать нужный сценарий и просмотреть значения таблицы или графика при значениях варьируемых переменных сценария. Такими сценариями могут быть оптимистический сценарий, пессимистический сценарий и другие. При нажатии в окне на Закончить пакет возвращается к текущему значению.
[9]
Ей показалось, что он может оказаться поворотным для любого пессимистического сценария развития событий.
[10]
Но если ваша цель состоит в том, чтобы исследовать пессимистические сценарии, вам следует сохранить разум незамутненным. Вы не должны отклонять ни одну потенциальную возможность. В частности, вы должны исследовать те рынки, которые центральные банки не контролируют, которые не подвластны административным средствам контроля над рынками, их нельзя даже закрыть.
[11]
Поэтому анализ чувствительности следует производить последовательно ( поэтапно) для наиболее вероятного, реалистического, а также оптимистического и пессимистического сценария.
[13]
В большинстве случаев достаточно разработать три альтернативных сценария: оптимистический, пессимистический и средний. В оптимистическом сценарии описываются события, которые могут произойти при наилучшем стечении обстоятельств, положительной и наиболее благоприятной динамике основных показателей и параметров ситуации. В пессимистическом сценарии высказываются предположения о возможном развитии событий при наименее благоприятных условиях и наихудших параметрах, характеризующих ситуацию в будущем. В среднем сценарии рассматривается прогноз развития событий в неких средних условиях, при которых одни параметры имеют положительную, другие — отрицательную тенденцию изменений. На основе сценариев могут быть подготовлены планы определенных действий, которые следует предпринять в различных условиях. Эти планы являются основой для принятия и реализации решений при развитии событий, предусмотренных по тому или иному сценарию.
[14]
Если, например, для всех сценариев значение NPV положительно, то проект, безусловно, может быть принят. Если значение NPV для всех сценариев отрицательно, то проект должен быть отклонен. При отрицательном значении NPV для пессимистического сценария следует оценить размер возможных потерь и принимать решение с использованием рассмотренных ранее шкал риска.
[15]
Страницы:
1
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Стратегия
План Б: Зачем компаниям несколько сценариев развития
Дмитрий Махлин
28 апреля 2022
В трудных ситуациях руководители обычно просчитывают все возможные риски. Но в случае неопределенности, которая не поддается прогнозу, эти расчеты могут оказаться ошибочными. Добавить определенности поможет построение сценариев и проработка плана конкретных действий внутри каждого из них. Классические сценарии — пессимистичный, реалистичный (базовый) и оптимистичный. Например, в HRLink мы заложили в пессимистичный сценарий нулевые продажи в течение трех месяцев, в реалистичный — падение продаж на 50%, в оптимистичный — выполнение ранее намеченного плана продаж.
План действий отражает, что мы будем делать в каждой ситуации, чтобы сохранить штат сотрудников. Это приоритет, потому что кризисы проходят, а репутация и человеческий капитал — те ценности, которые помогают преодолеть сложные времена и двигаться дальше. В каждом сценарии нужно понять, насколько хватит финансовой подушки для поддержания бизнеса, а также расставить приоритеты по издержкам. Коммерческие расходы — те, что связаны с продажей основного продукта, — должны быть первостепенными. Их сокращают в последнюю очередь. Расходы на бэк-офис и бонусы топ-менеджеров идут под нож практически сразу.
Например, мы разделили затраты по степени важности. Первое и главное — персонал. Второе — все, что касается маркетинга, PR, софта и других поддерживающих бизнес функций. Третье — выпуск брендированной продукции, фрукты в офис и другие приятные мелочи.
Важно честно рассказывать команде о том, какие варианты развития ситуации возможны в компании. Нужно говорить, какие меры принимаются руководством, чтобы стабилизировать ситуацию и снизить риски. Если кто-то из сотрудников не поддерживает выбранную тактику и не готов подстраиваться под изменения, лучше расстаться сразу. В сложные времена как никогда важен командный дух и единство в коллективе.
Внутренние коммуникации: открытость и персонализация
В условиях неопределенности сейчас оказались не только компании, но и люди. Важно помочь сотрудникам снизить стресс, добавив предсказуемости и порядка в корпоративные процессы. Потребуются следующие меры.
Внесение изменений в правила корпоративной этики. В новых реалиях нужно запретить обсуждать в рабочих чатах или на совещаниях политические, религиозные или другие острые темы. Надо объяснить, что все люди разные и каждый имеет право на собственное мнение, но работа — это не место для дебатов. Нарушения этого правила нужно жестко пресекать.
Реальная помощь сотрудникам. Многие столкнулись с проблемами: кто-то испытывает стресс и подавленность, кто-то не знает, как сохранить сбережения. Хорошо, если есть возможность пригласить для консультаций психолога или провести уроки финансовой грамотности, на которых каждый сможет разобрать свою ситуацию с экспертом.
Руководитель должен показывать людям, что жизнь продолжается несмотря ни на что. Важно сохранить привычный распорядок и регулярные активности. Например, проводить обучение, тренинги, корпоративные мероприятия или неформальные встречи. В условиях сокращения расходов тренерами могут выступать топ-менеджеры, собственники или инвесторы компании. Например, в начале марта один из наших учредителей проводил тренинг по продажам. Также мы организовали занятия по английскому языку для всех желающих.
Индивидуальный подход к каждому сотруднику. Такой подход помогает поддержать людей и в итоге повышает их лояльность компании. Если человек испытывает стресс и ему нужен дополнительный выходной — нужно его дать. Если есть объективные причины для пересмотра плана продаж — нужно его пересмотреть. Я провел встречу с каждым продавцом: мы проанализировали текущее количество и статус клиентов, изменили план продаж в зависимости от реальной картины. При этом каждый сам определял показатели.
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Предложения со словосочетанием «пессимистический сценарий»
Так что у этого сборника, кроме прогнозов, есть ещё и другая задача – вселить в нас надежду, показав, как наука может смягчить последствия пессимистических сценариев развития событий, включая негативное влияние изменения климата, перенаселённости и пандемий, вызванных устойчивостью микроорганизмов к антибиотикам.
Вполне возможно, что мои оценки и предположения о стремительном росте вероятности глобальных и локальных экологических катастроф на планете излишне пессимистичны, но при оценках экологического риска рассмотрение наиболее пессимистического сценария или наиболее тяжёлого варианта развития опасных событий обязательно и это абсолютно верно.
И только в последние десятилетия стала понятной обществу неотвратимость пессимистического сценария грядущего.
Несмотря на многочисленные пессимистические сценарии постпандемического мира, у нас есть основания для надежд на то, что после пандемии мы проснёмся не в городах-тюрьмах, а в комфортных городах, связанных современными сервисами, пользоваться которыми мы наконец научились, и обнаружим, что стали более внимательными друг к другу и к среде своего обитания.
Квинтэссенцией пессимистического сценария является абсолютное торжество победы зла над добром.
Привет! Меня зовут Лампобот, я компьютерная программа, которая помогает делать
Карту слов. Я отлично
умею считать, но пока плохо понимаю, как устроен ваш мир. Помоги мне разобраться!
Спасибо! Я стал чуточку лучше понимать мир эмоций.
Вопрос: кроющий — это что-то нейтральное, положительное или отрицательное?
Оптимистический и пессимистический сценарии развития банка должны разрабатываться с учётом их возможной реализации в случае отклонения (от базовых) индикаторов развития внешней и внутренней среды деятельности банка и соответствовать требованиям его акционеров.
Такая полярность: с одной стороны, подозрительность и забитость, а с другой стороны, безответственность и отвязность, – побуждает большинство душ к выбору наиболее пессимистического сценария.
Ассоциации к слову «сценарий»
Синонимы к слову «пессимистический»
Синонимы к слову «сценарий»
Сочетаемость слова «сценарий»
- подобный сценарий
новый сценарий
жизненный сценарий - сценарий фильма
сценарий игры
сценарий развития событий - автор сценария
написание сценария
основа сценария - сценарий повторился
сценарий понравился
сценарий назывался - написать сценарий
идти по сценарию
развиваться по сценарию - (полная таблица сочетаемости)
Значение слова «пессимистический»
-
ПЕССИМИСТИ́ЧЕСКИЙ, —ая, —ое. Проникнутый пессимизмом; унылый, мрачный. Пессимистический взгляд. (Малый академический словарь, МАС)
Все значения слова ПЕССИМИСТИЧЕСКИЙ
Значение слова «сценарий»
-
СЦЕНА́РИЙ, -я, м. 1. Литературно-драматическое произведение (содержащее подробное описание действия и текст речей персонажей), на основе которого создается фильм. (Малый академический словарь, МАС)
Все значения слова СЦЕНАРИЙ
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Pessimistic Scenarios
John F. Shroder, in Natural Resources in Afghanistan, 2014
Abstract
Pessimistic scenarios for Afghanistan’s future rest particularly on the prospects for ongoing incessant war in the country because of a long list of such things as Pashtun cultural characteristics; neo-environmental determinism with a harsh physical environment leading to environmental and developmental problems; charismatic or “mad” mullahs who encourage bad behaviors; strong ethnic patchworks leading to ethnic cleansing, and the like; repeated foreign invasions; Afghan xenophobia; endemic corruption; land-ownership disputes; Islamicist desire for endless jihad; decades of war, destroyed educational systems, reduced capacity, widespread illiteracy, drug culture, and the purdah culture wherein valuable women are excluded from normal and useful problem solving. In addition are the paranoia, insecurity, and inability of nearby Pakistan to gain the upper hand over the ISI-driven and protected Taliban insurgency enjoying largely unmolested safe haven there, coupled with similar but not as extreme difficulties with fanatically religious Iran as well: the existing environmental basket case of Afghanistan only contributes further fuel to the pessimism. None of these problems is sufficient by itself to keep Afghanistan down, but in aggregate, they seem to have been enough to spoil the country up to now.
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URL:
https://www.sciencedirect.com/science/article/pii/B9780128001356000209
Renewable energy integration as an alternative to the traditional ground-source heat pump system
Cristina Sáez Blázquez, … Diego González-Aguilera, in Energy Services Fundamentals and Financing, 2021
5.5.1.3 CO2 emission factor
The last variable considered in the sensitivity analysis is the CO2 emission factor. Observing Table 5.10, the expected emission factor is 0.399. As in the previous cases, optimistic and pessimistic scenarios have also been analyzed. Thus this factor has been reduced and increased in a 15%, obtaining for the optimistic situation an emission factor of 0.339 and 0.459 for the pessimistic one. Fig. 5.12 shows how the sensitivity to the increase or reduction of the emission factor is approximately the same in both systems. However, as in the previous cases, the sensitivity of the conventional system is higher compared to the suggested one.
Figure 5.12. Sensitivity analysis to the variation of the CO2 emission factor. Op, optimistic scenario; Ex, expected scenario; Pe, pessimistic scenario.
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https://www.sciencedirect.com/science/article/pii/B9780128205921000051
Pathophysiology and Pathogenesis of Diabetic Nephropathy
Gunter Wolf, … Fuad N. Ziyadeh, in Seldin and Giebisch’s The Kidney (Fourth Edition), 2008
REGRESSION
One would assume that the morphological alterations of advanced diabetic nephropathy (e.g. glomerulosclerosis, interstitial fibrosis) are not reversible and represent a oneway road to ESRD. Experimental (32) and clinical studies (66) show, however, that this pessimistic scenario is not necessarily true. Several studies indicate that aggressive blood pressure control with agents that interfere with the RAAS leads to remission of overt proteinuria. Fioretto and colleagues performed serial renal biopsy in eight patients with type 1 diabetes and nephropathy who had undergone a successful pancreas transplantation (66). These investigators found that the morphological alterations of diabetic nephropathy, including increased GBM thickness and mesangial matrix expansion, decreased 10 years after receiving the pancreas transplant. The data clearly demonstrate that the sustained euglycemia can reverse the development of diabetic nephropathy, but this process takes time. In addition, Perkins et al. (180) showed that the onset of sustained microalbuminuria does not imply inexorably progressive nephropathy, at least not in patients with type 1 diabetes. This study provides indirect evidence that aggressive management aiming at various risk factors could lead to remission of albuminuria. Since no renal biopsies were performed in this study, it remained unproven whether morphological changes of diabetic nephropathy were also improved. Although the molecular mechanisms surrounding regression are currently incompletely understood, it is possible that induction of metalloproteinases that may degrade the deposited extracellular matrix play a central role in this process. Nevertheless, there is light at the end of the tunnel and a better understanding of the complex pathophysiology of diabetic nephropathy will likely improve a multipronged approach to induce regression.
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https://www.sciencedirect.com/science/article/pii/B9780120884889500814
Socio-economic and environmental assessment of concentrating solar power systems
Natalia CaldésYolanda Lechón, in Concentrating Solar Power Technology (Second Edition), 2021
5.4.1 Environmental impacts projections
The evolution of the GHG and other emissions over time is evaluated in the work performed in the NEEDS project (Viebahn et al., 2011). When assessing the emissions of future configurations of CSP power plants, a clear reduction is observed showing an ‘environmental learning’ of the technology. The three development scenarios: very optimistic, optimistic–realistic, and pessimistic scenarios refer to different assumptions on the anticipated penetration of the technology into the energy market reaching an installed capacity in 2050 of 1000 GW, 405 GW, and 120 GW, respectively. In these scenarios the prevailing CSP technology differs. Greenhouse gas emissions show a large reduction throughout the scenario development. The reason is the reduction of salt used in the different storage systems. Concrete storage or PCM storage based power plants performs better than the current molten salt based ones. The results show a continuous optimization from the ‘pessimistic’ to the ‘very optimistic’ scenario as well as over time.
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https://www.sciencedirect.com/science/article/pii/B9780128199701000037
27th European Symposium on Computer Aided Process Engineering
Kathleen B. Aviso, … Raymond R. Tan, in Computer Aided Chemical Engineering, 2017
4.1 Reducing Emission through Pollution Control Technologies
This case study considers the selection of pollution control technologies for the reduction in hydrogen fluoride emission in the production of clay bricks such that the emission reduction target is met at the lowest cost. The data used for this case study was obtained from Kantardgi et al. (2006). The data for the emissions reduction technologies are shown in Table 1. Each pollution control technology i is able to reduce the emission of hydrogen fluoride by Ri and has a corresponding capital cost of Ci.
Table 1. Emission reduction potential and capital costs of emission technologies
| Option | Technology Option | % Reduction (Ri) | %Ri Average | Capital Cost in (× 1000 US$) (Ci) | Maximum Capital Cost (× 1000 US$) |
|---|---|---|---|---|---|
| 1 | Modifying the time-temperature profile | 30 – 60 | 45 | 37.8 | 37.8 |
| 2 | Reducing the airflow through the kiln | 30 – 80 | 55 | 37.8 – 151.2 | 151.2 |
| 3 | Increasing the turbulence in the preheat zone | 0 – 50 | 25 | 37.8 – 189 | 189 |
| 4 | Increasing the interaction between the product and the flue gas | 30 – 90 | 60 | 94.5 | 94.5 |
| 5 | Utilizing the flue gas | 30 – 60 | 45 | 3.78 | 3.78 |
| 6 | Additives fluoride reactive compound limestone | 0 – 80 | 40 | 3.78 – 189 | 189 |
| 7 | Dry bed limestone scrubber | 95 | 95 | 226.8 – 945 | 945 |
| 8 | Dry cloth filter scrubber | 95 | 95 | 756 – 1,323 | 1,323 |
| 9 | Wet scrubber | 95 | 95 | 226.8 – 1,323 | 1,323 |
Tan (2007) solved the same case study using the average values of emission reduction potential and the maximum possible capital cost to achieve a minimum emission reduction of 90% (Y = 0.90); this solution is used as Scenario 1 here. This result is compared with the most pessimistic scenario (Scenario 2) where the technologies are assumed to perform with the lowest emission reduction and at the highest capital cost and the most optimistic scenario (Scenario 3) where the technologies perform at the highest emission reduction and the lowest capital cost. The results show that the selection of technologies varies with each scenario. Scenario 1 is able to reduce the emissions by 90% at a cost of US$ 249,480 and selecting technologies 2, 4 and 5. The most optimistic scenario can reach an emission reduction of practically 100% at a cost of US$ 306,180 and selects pollution technologies 1 to 6. Alternatively, the most pessimistic scenario achieves an emission reduction of 90% with an associated cost of US$ 532,980 and selects technologies 1, 2, 4, 5 and 6.
The problem is further analyzed using a Target Oriented Robust Optimization (TORO) model and Monte Carlo simulation in order to identify a robust combination of technologies which takes into consideration the risks associated with the uncertainties in cost and in emission reduction.
The results are summarized in Table 2 where the costs are reported in thousand US$. The parameter % P corresponds to the probability of constraint achievement, while % X corresponds to the emission reduction achieved.
Table 2. Results of TORO optimization as a function of robustness index (θ) for Case 1
The TORO formulation simultaneously considers the existing uncertainties in the cost and emission reduction potential. Based on the results summarized in Table 2, the best solution is to select technologies 1 to 6 since it achieves an emission reduction of 98 %. This solution corresponds to the robustness index of 0.50 to 0.80 and corresponds to the shaded cells found in Table 2. Furthermore, the performance of this solution is expected to have an average cost below the expected cost target level if uncertainties are considered and that constraints will be met to a probability of 80 %.
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Magnitude Coding
Olivier Rance, … Pierre Lemaître-Auger, in RCS Synthesis for Chipless RFID, 2017
4.3.4 Partial conclusion – tags with ground planes
A complete study has been realized to assess the feasibility and the potential gain offered by magnitude coding in the context of chipless RFID. From a comparative study, the simplest way to control the magnitude has been determined. This approach, based on the polarization mismatch between the tag and the antennas, is easy to implement in practice because it only consists of modifying the orientation of the resonators individually, which does not add any particular difficulty when designing the tag. Based on a mostly pessimistic scenario, we estimate a 15-bit coding capacity for a tag made up of four frequency-coded resonators. Magnitude coding has made it possible to increase the coding capacity by 9 bits. It has been shown that the presence of a reference scatterer included in the tag makes it possible to realize measurements with a minimalist calibration process and without depending on the read range.
One limiting aspect that has not been addressed is the influence of the absence of knowledge beforehand of the orientation of the tag in relation to the reader. However, it is theoretically possible to find this information using indirect approaches by implementing them either at the level of the reader or at the level of the tag. For example, an interesting approach consists of comparing the response of two reference resonators that have different, known orientations.
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https://www.sciencedirect.com/science/article/pii/B978178548144450004X
Case Studies Worldwide
Susan Hanson, … Simon D. Rundle, in Coastal Risk Management in a Changing Climate, 2015
Flooding
SLR induces a shift of the PDF such that higher values are more probable. Hence, it can be deduced that logically the maximum flooding elevation will rise in all the beaches. The distortion of the PDFs are associated mainly to changes in mean sea level, increasing the probability of exceedance of a given beach level. As an example, Figure 7.109 shows the PDF of coastal flooding in Sardinero 2 under the present climate conditions, the moderate change (SLR = +0.2 m), the high change (SLR = +0.4 m), and the pessimistic scenario (SLR = +1 m).
Figure 7.109. PDF of coastal flooding in Sardinero two for the different scenarios of sea-level rise: the present conditions (dark line), moderate change for year 2050 (+0.2 m), high change for year 2080 (+0.4 m), and pessimistic scenario for year 2100 (+1 m).
Besides, the representative parameters (location, scale, and shape) of the GEV distribution for the six beaches and the scenarios analyzed are summarized in Table 7.75.
TABLE 7.75. Summary of the Location, Scale, and Shape Parameters of the GEV Distributions for the Six Beaches Analyzed and Scenarios Proposed
| Location Parameter (μ) | Shape Parameter (ε) | Scale Parameter (Ψ) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Present climate | +0.2 | +0.4 | +1 | Present climate | +0.2 | +0.4 | +1 | Present climate | +0.2 | +0.4 | +1 | |
| Sardinero 2 | 7.81 | 8.01 | 8.21 | 8.81 | −0.25 | −0.25 | −0.25 | −0.25 | 0.6 | 0.6 | 0.6 | 0.6 |
| Sardinero 1 | 7.72 | 7.92 | 8.12 | 8.72 | −0.26 | −0.26 | −0.26 | −0.26 | 0.35 | 0.35 | 0.35 | 0.35 |
| Loredo | 8.62 | 8.82 | 9.02 | 9.62 | −0.25 | −0.25 | −0.25 | −0.25 | 0.43 | 0.43 | 0.43 | 0.43 |
| Somo | 8.08 | 8.28 | 8.48 | 9.08 | −0.24 | −0.24 | −0.24 | −0.24 | 0.38 | 0.38 | 0.38 | 0.38 |
| Puntal | 7.38 | 7.58 | 7.78 | 8.38 | −0.28 | −0.28 | −0.28 | −0.28 | 0.3 | 0.3 | 0.3 | 0.3 |
| Peligros | 6.59 | 6.79 | 6.99 | 7.59 | −0.33 | −0.33 | −0.33 | −0.33 | 0.22 | 0.22 | 0.22 | 0.22 |
The flooding analysis in the inner bay has been carried out using the pragmatic approach described in section 7.9.3.4. The scenarios summarized in Table 7.76 that combine extreme flooding levels with relative SLR projections have been used to assess the flooding in Santander Bay.
TABLE 7.76. Summary of Flooding Scenarios
| Scenario | Year of Return Period | Flooding Level (cm) | Relative Sea-Level Rise (cm) | Total Sea Level | Horizon Year |
|---|---|---|---|---|---|
| A | 50 | 256 | 20 | 276 | 2050 |
| B | 50 | 256 | 40 | 296 | 2080 |
| C | 50 | 256 | 100 | 356 | 2080 |
| D | 200 | 276 | 20 | 296 | 2050 |
| E | 200 | 276 | 40 | 316 | 2080 |
| F | 200 | 276 | 100 | 376 | 2080 |
Figure 7.110 shows the flooding levels associated with a return period of 50 years and an SLR of +1 m (scenario C in Table 7.76). As can be seen in this figure, several areas will be affected by flooding, such as various areas of the Cubas sea inlet (see Figure 7.110(c)). These areas are currently reclaimed, but in the past belonged to the intertidal area of the estuary. Other areas affected belong to Alday marshes (see Figure 7.110(a)). At present, these marshes are regulated hydrodynamically by means of a system of sluices (see Figure 7.110(b)).
Figure 7.110. Different areas affected by flood level in Santander Bay for scenario C.
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Uncertainty Issues in Biomass-Based Production Chains
Şebnem Yılmaz Balaman, in Decision-Making for Biomass-Based Production Chains, 2019
5.2.2.2 Scenario Analyses
Scenario analysis is conducted, to analyze the impacts of possible future events on the system performance by taking into account several alternative outcomes, i.e., scenarios, and to present different options for future development paths resulting in varying outcomes and corresponding implications. Scenario analysis is the process of forecasting the expected value of a performance indicator, given a time period, occurrence of different situations, and related changes in the values of system parameters under an uncertain environment. Scenario analysis can be used to estimate the behavior of the system in response to an unexpected event, and may be utilized to explore the changes in system performance, in a theoretical best-case (optimistic) or worst-case (pessimistic) scenario. The occurrence probability and possible impact of a scenario should be considered in tandem to develop a strategic plan base on scenario analysis results. The major aim is, to analyze the results of the more extreme outcomes (with high probability and/or more severe impacts), to determine the investment strategy.
In design and management of biomass-based production chains, a decision-maker might use scenario analysis to estimate the impacts of several possible scenarios regarding changes in bio-fuel sale prices (increased, reduced, or constant prices), on the performance of the chain. Another analysis may depend on governmental strategies on incentivizing production from renewable sources or specific incentives for carbon sequestering operations. Different incentive policies may be considered to evaluate the behavior and performance of the production chain under changing financial and economic circumstances. The subsets of each of the possibilities and the correlations between these subsets may be taken into account, and the scenario-weighted expected profitability of the production chain may be calculated.
The complexity of the problem, and the existence of stakeholders and related conflicting objectives in a supply chain, may make the scenario analysis a challenging practice. It may be difficult to forecast future events and corresponding impacts and assign probabilities to them. The system may need to be modeled by capturing possible fluctuations within a single scenario or possible correlations between multiple scenarios, which make the analysis further complex.
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https://www.sciencedirect.com/science/article/pii/B9780128142783000054
24th European Symposium on Computer Aided Process Engineering
Chinedu O. Okoli, Thomas A. AdamsII, in Computer Aided Chemical Engineering, 2014
4.2 Economic results
These results are intuitive, as the feedstock cost makes up a major percentage of the variable operating cost while the mixed alcohols co-product makes up almost half of the products. In order to consider the effects of multiple parameters which change at the same time, different scenarios in which the most significant parameters vary simultaneously are considered as shown in Table 3. The results show that in the “optimistic” to “pessimistic” scenario range of 0.55 — 1.17 $/L the process remains competitive, especially in comparison to ABE-derived butanol prices which range from 0.59 $/L (Kumar et al., 2012) to 1.05 $/L (Qureshi et al., 2013). An important assumption in this study is the alcohol synthesis catalyst CO-conversion of 40 %. This value is in line with the National Renewable Energy Laboratory target of greater than 50 % CO-conversion for alcohol synthesis catalysts, achievable from future research advances (Phillips et al., 2007). However the catalyst used in this study has a CO-conversion of 8.5 % (Herman, 2000), a value that results in an MBSP of 1.60 $/L which is unlikely to be competitive. Furthermore, the base case MBSP result of 0.84 $/L in comparison to the gasoline price of 0.82 $/Lbeq is intuitive, as the MBSP should be worse than the gasoline price except for high crude oil price situations. Thus this process provides a method of producing 2nd generation biofuels at only a small premium over petroleum-derived gasoline, assuming the achievement of the target alcohol synthesis catalyst CO-conversion. In addition, it is important to note that the lignocellulose biomass-to-butanol ABE process has only been demonstrated at the laboratory scale, and thus the wide range of prices for ABE-derived butanol is highly uncertain as it is unlikely to be better than gasoline.
Table 3. Impact of different economic scenarios on the MBSP
| Scenarios – Parameter Values Considered | Very Optimistic | Optimistic | Base Case | Pessimistic | Very Pessimistic |
|---|---|---|---|---|---|
| Internal Rate of Return | 5 | 7.5 | 10 | 12.5 | 15 |
| Feedstock costs ($/dry t) | 60 | 68 | 75 | 83 | 90 |
| Mixed Alcohol value ($/L) | 0.85 | 0.77 | 0.69 | 0.62 | 0.54 |
| Total Depreciable Capital | 230 | 279 | 328 | 377 | 426 |
| Resulting MBSP ($/L) | 0.29 | 0.55 | 0.84 | 1.17 | 1.54 |
Table 2. Breakdown of capital cost
| Capital cost calculations | $ Million |
|---|---|
| Total Installed Equipment | |
| Cost | 214 |
| Total Indirect Cost | 114 |
| Total Depreciable Capital | 328 |
| Royalties | 7 |
| Land | 6 |
| Fixed Capital Investment | 341 |
| Working Capital | 17 |
| Total Capital Investment | 358 |
Figure 2. Sensitivity of the MBSP (base case of 0.84 $/L) to changes in key parameters
Finally, an area to consider in future research is the impact of cheaper feedstocks, such as switchgrass and wheat straw, on the thermochemical biobutanol MBSP. These feedstocks will have different performance metrics, as they will form different syngas compositions with undetermined ripple effects through the process which will have to be evaluated.
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26th European Symposium on Computer Aided Process Engineering
Anvitha Kandiraju, … Ignacio E. Grossmann, in Computer Aided Chemical Engineering, 2016
5 Illustrative Example
The proposed formulation is tested on an a small instance from the air separation industry. This instance has two facilities of the leader and three facilities of the independent suppliers, satisfying demands of 10 markets over a period of 30 quarters (7.5 years). Facilities from the leader have a capacity of 75,000 tons/period and 20,000 tons/period. Independent suppliers have capacities of 100,000 tons/period, 70,000 tons/period and 20,000 tons/period. The capacity expansion limit for each plant is 9000 tons/period. All the capacities are shared between two products with production ratios of 15-25% for product-1 and 75-85% for product-2 with respect to the overall production. There are three demand scenarios that can occur: pessimistic scenario in which the demand drops from 132,700 tons/period to 100,000 tons/period; nominal case where the demand increases from 132,700 tons/period to 161,000 tons/period; and an optimistic case in which the demand further increases from 132,700 tons/period to 256,600 tons/period. Discount rate of 3% is used. Fixed and operating costs increase with time, which are not elaborated here due to space restrictions. We plan the capacity strategy for the leader under the above mentioned conditions.
We consider a stochastic programming model and the equivalent deterministic case in order to compare the performance of the models. The deterministic case is obtained by taking an average over the three demand scenarios. The expansion strategy obtained from the deterministic case is enforced on the stochastic model to analyze the impact of ignoring demand uncertainty. The results are presented in Table 1.
Table 1. Comparison of the results for the deterministic and the stochastic formulations
| Term in objective function | Deterministic solution | Stochastic solution | Deterministic solution under uncertainty |
|---|---|---|---|
| Revenue (MM$): | 1,548 | 1,331 | 1,460 |
| New facilities (MM$): | 0 | 0 | 0 |
| Maintenance (MM$): | 127 | 127 | 127 |
| Expansion (MM$): | 187 | 96 | 187 |
| Production (MM$): | 660 | 568 | 625 |
| Transportation (MM$): | 130 | 120 | 124 |
| NPV (MM$): | 444 | 420 | 397 |
It can be seen from Table1 that the deterministic model gives higher NPV for an average demand case. However, when deterministic solutions are enforced in the face of uncertainties, the model gives lower expected NPV ($397MM) in comparison to the stochastic model ($420MM).
The deterministic model makes aggressive capacity expansion decisions while the stochastic programming model is more conservative. This is because of the high probability of pessimistic and nominal demand scenarios. Considering all demand scenarios in the formulation mitigates the risk associated to demand uncertainties.
The MILP reformulations were implemented in GAMS 24.4.6 and solved using CPLEX 12.6.2.0. The computational statistics for the models are presented in Table 2. The stochastic model has increased size compared to the deterministic case because of the additional scenarios. The increase involves more constraints and continuous variables, while the number of discrete variables remains the same as they are planning decisions which are shared for all scenarios.
Table 2. Model statistics for the proposed formulations
| Model statistic | Deterministic model | Stochastic model |
|---|---|---|
| Number of constraints: | 6,412 | 18,586 |
| Number of continuous variables: | 8,246 | 24,261 |
| Number of binary variables: | 146 | 146 |
| Solution time (s): | 49 | 55 |
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Pessimistic Scenarios
John F. Shroder, in Natural Resources in Afghanistan, 2014
Abstract
Pessimistic scenarios for Afghanistan’s future rest particularly on the prospects for ongoing incessant war in the country because of a long list of such things as Pashtun cultural characteristics; neo-environmental determinism with a harsh physical environment leading to environmental and developmental problems; charismatic or “mad” mullahs who encourage bad behaviors; strong ethnic patchworks leading to ethnic cleansing, and the like; repeated foreign invasions; Afghan xenophobia; endemic corruption; land-ownership disputes; Islamicist desire for endless jihad; decades of war, destroyed educational systems, reduced capacity, widespread illiteracy, drug culture, and the purdah culture wherein valuable women are excluded from normal and useful problem solving. In addition are the paranoia, insecurity, and inability of nearby Pakistan to gain the upper hand over the ISI-driven and protected Taliban insurgency enjoying largely unmolested safe haven there, coupled with similar but not as extreme difficulties with fanatically religious Iran as well: the existing environmental basket case of Afghanistan only contributes further fuel to the pessimism. None of these problems is sufficient by itself to keep Afghanistan down, but in aggregate, they seem to have been enough to spoil the country up to now.
Read full chapter
URL:
https://www.sciencedirect.com/science/article/pii/B9780128001356000209
Renewable energy integration as an alternative to the traditional ground-source heat pump system
Cristina Sáez Blázquez, … Diego González-Aguilera, in Energy Services Fundamentals and Financing, 2021
5.5.1.3 CO2 emission factor
The last variable considered in the sensitivity analysis is the CO2 emission factor. Observing Table 5.10, the expected emission factor is 0.399. As in the previous cases, optimistic and pessimistic scenarios have also been analyzed. Thus this factor has been reduced and increased in a 15%, obtaining for the optimistic situation an emission factor of 0.339 and 0.459 for the pessimistic one. Fig. 5.12 shows how the sensitivity to the increase or reduction of the emission factor is approximately the same in both systems. However, as in the previous cases, the sensitivity of the conventional system is higher compared to the suggested one.
Figure 5.12. Sensitivity analysis to the variation of the CO2 emission factor. Op, optimistic scenario; Ex, expected scenario; Pe, pessimistic scenario.
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Pathophysiology and Pathogenesis of Diabetic Nephropathy
Gunter Wolf, … Fuad N. Ziyadeh, in Seldin and Giebisch’s The Kidney (Fourth Edition), 2008
REGRESSION
One would assume that the morphological alterations of advanced diabetic nephropathy (e.g. glomerulosclerosis, interstitial fibrosis) are not reversible and represent a oneway road to ESRD. Experimental (32) and clinical studies (66) show, however, that this pessimistic scenario is not necessarily true. Several studies indicate that aggressive blood pressure control with agents that interfere with the RAAS leads to remission of overt proteinuria. Fioretto and colleagues performed serial renal biopsy in eight patients with type 1 diabetes and nephropathy who had undergone a successful pancreas transplantation (66). These investigators found that the morphological alterations of diabetic nephropathy, including increased GBM thickness and mesangial matrix expansion, decreased 10 years after receiving the pancreas transplant. The data clearly demonstrate that the sustained euglycemia can reverse the development of diabetic nephropathy, but this process takes time. In addition, Perkins et al. (180) showed that the onset of sustained microalbuminuria does not imply inexorably progressive nephropathy, at least not in patients with type 1 diabetes. This study provides indirect evidence that aggressive management aiming at various risk factors could lead to remission of albuminuria. Since no renal biopsies were performed in this study, it remained unproven whether morphological changes of diabetic nephropathy were also improved. Although the molecular mechanisms surrounding regression are currently incompletely understood, it is possible that induction of metalloproteinases that may degrade the deposited extracellular matrix play a central role in this process. Nevertheless, there is light at the end of the tunnel and a better understanding of the complex pathophysiology of diabetic nephropathy will likely improve a multipronged approach to induce regression.
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Socio-economic and environmental assessment of concentrating solar power systems
Natalia CaldésYolanda Lechón, in Concentrating Solar Power Technology (Second Edition), 2021
5.4.1 Environmental impacts projections
The evolution of the GHG and other emissions over time is evaluated in the work performed in the NEEDS project (Viebahn et al., 2011). When assessing the emissions of future configurations of CSP power plants, a clear reduction is observed showing an ‘environmental learning’ of the technology. The three development scenarios: very optimistic, optimistic–realistic, and pessimistic scenarios refer to different assumptions on the anticipated penetration of the technology into the energy market reaching an installed capacity in 2050 of 1000 GW, 405 GW, and 120 GW, respectively. In these scenarios the prevailing CSP technology differs. Greenhouse gas emissions show a large reduction throughout the scenario development. The reason is the reduction of salt used in the different storage systems. Concrete storage or PCM storage based power plants performs better than the current molten salt based ones. The results show a continuous optimization from the ‘pessimistic’ to the ‘very optimistic’ scenario as well as over time.
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27th European Symposium on Computer Aided Process Engineering
Kathleen B. Aviso, … Raymond R. Tan, in Computer Aided Chemical Engineering, 2017
4.1 Reducing Emission through Pollution Control Technologies
This case study considers the selection of pollution control technologies for the reduction in hydrogen fluoride emission in the production of clay bricks such that the emission reduction target is met at the lowest cost. The data used for this case study was obtained from Kantardgi et al. (2006). The data for the emissions reduction technologies are shown in Table 1. Each pollution control technology i is able to reduce the emission of hydrogen fluoride by Ri and has a corresponding capital cost of Ci.
Table 1. Emission reduction potential and capital costs of emission technologies
| Option | Technology Option | % Reduction (Ri) | %Ri Average | Capital Cost in (× 1000 US$) (Ci) | Maximum Capital Cost (× 1000 US$) |
|---|---|---|---|---|---|
| 1 | Modifying the time-temperature profile | 30 – 60 | 45 | 37.8 | 37.8 |
| 2 | Reducing the airflow through the kiln | 30 – 80 | 55 | 37.8 – 151.2 | 151.2 |
| 3 | Increasing the turbulence in the preheat zone | 0 – 50 | 25 | 37.8 – 189 | 189 |
| 4 | Increasing the interaction between the product and the flue gas | 30 – 90 | 60 | 94.5 | 94.5 |
| 5 | Utilizing the flue gas | 30 – 60 | 45 | 3.78 | 3.78 |
| 6 | Additives fluoride reactive compound limestone | 0 – 80 | 40 | 3.78 – 189 | 189 |
| 7 | Dry bed limestone scrubber | 95 | 95 | 226.8 – 945 | 945 |
| 8 | Dry cloth filter scrubber | 95 | 95 | 756 – 1,323 | 1,323 |
| 9 | Wet scrubber | 95 | 95 | 226.8 – 1,323 | 1,323 |
Tan (2007) solved the same case study using the average values of emission reduction potential and the maximum possible capital cost to achieve a minimum emission reduction of 90% (Y = 0.90); this solution is used as Scenario 1 here. This result is compared with the most pessimistic scenario (Scenario 2) where the technologies are assumed to perform with the lowest emission reduction and at the highest capital cost and the most optimistic scenario (Scenario 3) where the technologies perform at the highest emission reduction and the lowest capital cost. The results show that the selection of technologies varies with each scenario. Scenario 1 is able to reduce the emissions by 90% at a cost of US$ 249,480 and selecting technologies 2, 4 and 5. The most optimistic scenario can reach an emission reduction of practically 100% at a cost of US$ 306,180 and selects pollution technologies 1 to 6. Alternatively, the most pessimistic scenario achieves an emission reduction of 90% with an associated cost of US$ 532,980 and selects technologies 1, 2, 4, 5 and 6.
The problem is further analyzed using a Target Oriented Robust Optimization (TORO) model and Monte Carlo simulation in order to identify a robust combination of technologies which takes into consideration the risks associated with the uncertainties in cost and in emission reduction.
The results are summarized in Table 2 where the costs are reported in thousand US$. The parameter % P corresponds to the probability of constraint achievement, while % X corresponds to the emission reduction achieved.
Table 2. Results of TORO optimization as a function of robustness index (θ) for Case 1
The TORO formulation simultaneously considers the existing uncertainties in the cost and emission reduction potential. Based on the results summarized in Table 2, the best solution is to select technologies 1 to 6 since it achieves an emission reduction of 98 %. This solution corresponds to the robustness index of 0.50 to 0.80 and corresponds to the shaded cells found in Table 2. Furthermore, the performance of this solution is expected to have an average cost below the expected cost target level if uncertainties are considered and that constraints will be met to a probability of 80 %.
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Magnitude Coding
Olivier Rance, … Pierre Lemaître-Auger, in RCS Synthesis for Chipless RFID, 2017
4.3.4 Partial conclusion – tags with ground planes
A complete study has been realized to assess the feasibility and the potential gain offered by magnitude coding in the context of chipless RFID. From a comparative study, the simplest way to control the magnitude has been determined. This approach, based on the polarization mismatch between the tag and the antennas, is easy to implement in practice because it only consists of modifying the orientation of the resonators individually, which does not add any particular difficulty when designing the tag. Based on a mostly pessimistic scenario, we estimate a 15-bit coding capacity for a tag made up of four frequency-coded resonators. Magnitude coding has made it possible to increase the coding capacity by 9 bits. It has been shown that the presence of a reference scatterer included in the tag makes it possible to realize measurements with a minimalist calibration process and without depending on the read range.
One limiting aspect that has not been addressed is the influence of the absence of knowledge beforehand of the orientation of the tag in relation to the reader. However, it is theoretically possible to find this information using indirect approaches by implementing them either at the level of the reader or at the level of the tag. For example, an interesting approach consists of comparing the response of two reference resonators that have different, known orientations.
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Case Studies Worldwide
Susan Hanson, … Simon D. Rundle, in Coastal Risk Management in a Changing Climate, 2015
Flooding
SLR induces a shift of the PDF such that higher values are more probable. Hence, it can be deduced that logically the maximum flooding elevation will rise in all the beaches. The distortion of the PDFs are associated mainly to changes in mean sea level, increasing the probability of exceedance of a given beach level. As an example, Figure 7.109 shows the PDF of coastal flooding in Sardinero 2 under the present climate conditions, the moderate change (SLR = +0.2 m), the high change (SLR = +0.4 m), and the pessimistic scenario (SLR = +1 m).
Figure 7.109. PDF of coastal flooding in Sardinero two for the different scenarios of sea-level rise: the present conditions (dark line), moderate change for year 2050 (+0.2 m), high change for year 2080 (+0.4 m), and pessimistic scenario for year 2100 (+1 m).
Besides, the representative parameters (location, scale, and shape) of the GEV distribution for the six beaches and the scenarios analyzed are summarized in Table 7.75.
TABLE 7.75. Summary of the Location, Scale, and Shape Parameters of the GEV Distributions for the Six Beaches Analyzed and Scenarios Proposed
| Location Parameter (μ) | Shape Parameter (ε) | Scale Parameter (Ψ) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Present climate | +0.2 | +0.4 | +1 | Present climate | +0.2 | +0.4 | +1 | Present climate | +0.2 | +0.4 | +1 | |
| Sardinero 2 | 7.81 | 8.01 | 8.21 | 8.81 | −0.25 | −0.25 | −0.25 | −0.25 | 0.6 | 0.6 | 0.6 | 0.6 |
| Sardinero 1 | 7.72 | 7.92 | 8.12 | 8.72 | −0.26 | −0.26 | −0.26 | −0.26 | 0.35 | 0.35 | 0.35 | 0.35 |
| Loredo | 8.62 | 8.82 | 9.02 | 9.62 | −0.25 | −0.25 | −0.25 | −0.25 | 0.43 | 0.43 | 0.43 | 0.43 |
| Somo | 8.08 | 8.28 | 8.48 | 9.08 | −0.24 | −0.24 | −0.24 | −0.24 | 0.38 | 0.38 | 0.38 | 0.38 |
| Puntal | 7.38 | 7.58 | 7.78 | 8.38 | −0.28 | −0.28 | −0.28 | −0.28 | 0.3 | 0.3 | 0.3 | 0.3 |
| Peligros | 6.59 | 6.79 | 6.99 | 7.59 | −0.33 | −0.33 | −0.33 | −0.33 | 0.22 | 0.22 | 0.22 | 0.22 |
The flooding analysis in the inner bay has been carried out using the pragmatic approach described in section 7.9.3.4. The scenarios summarized in Table 7.76 that combine extreme flooding levels with relative SLR projections have been used to assess the flooding in Santander Bay.
TABLE 7.76. Summary of Flooding Scenarios
| Scenario | Year of Return Period | Flooding Level (cm) | Relative Sea-Level Rise (cm) | Total Sea Level | Horizon Year |
|---|---|---|---|---|---|
| A | 50 | 256 | 20 | 276 | 2050 |
| B | 50 | 256 | 40 | 296 | 2080 |
| C | 50 | 256 | 100 | 356 | 2080 |
| D | 200 | 276 | 20 | 296 | 2050 |
| E | 200 | 276 | 40 | 316 | 2080 |
| F | 200 | 276 | 100 | 376 | 2080 |
Figure 7.110 shows the flooding levels associated with a return period of 50 years and an SLR of +1 m (scenario C in Table 7.76). As can be seen in this figure, several areas will be affected by flooding, such as various areas of the Cubas sea inlet (see Figure 7.110(c)). These areas are currently reclaimed, but in the past belonged to the intertidal area of the estuary. Other areas affected belong to Alday marshes (see Figure 7.110(a)). At present, these marshes are regulated hydrodynamically by means of a system of sluices (see Figure 7.110(b)).
Figure 7.110. Different areas affected by flood level in Santander Bay for scenario C.
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Uncertainty Issues in Biomass-Based Production Chains
Şebnem Yılmaz Balaman, in Decision-Making for Biomass-Based Production Chains, 2019
5.2.2.2 Scenario Analyses
Scenario analysis is conducted, to analyze the impacts of possible future events on the system performance by taking into account several alternative outcomes, i.e., scenarios, and to present different options for future development paths resulting in varying outcomes and corresponding implications. Scenario analysis is the process of forecasting the expected value of a performance indicator, given a time period, occurrence of different situations, and related changes in the values of system parameters under an uncertain environment. Scenario analysis can be used to estimate the behavior of the system in response to an unexpected event, and may be utilized to explore the changes in system performance, in a theoretical best-case (optimistic) or worst-case (pessimistic) scenario. The occurrence probability and possible impact of a scenario should be considered in tandem to develop a strategic plan base on scenario analysis results. The major aim is, to analyze the results of the more extreme outcomes (with high probability and/or more severe impacts), to determine the investment strategy.
In design and management of biomass-based production chains, a decision-maker might use scenario analysis to estimate the impacts of several possible scenarios regarding changes in bio-fuel sale prices (increased, reduced, or constant prices), on the performance of the chain. Another analysis may depend on governmental strategies on incentivizing production from renewable sources or specific incentives for carbon sequestering operations. Different incentive policies may be considered to evaluate the behavior and performance of the production chain under changing financial and economic circumstances. The subsets of each of the possibilities and the correlations between these subsets may be taken into account, and the scenario-weighted expected profitability of the production chain may be calculated.
The complexity of the problem, and the existence of stakeholders and related conflicting objectives in a supply chain, may make the scenario analysis a challenging practice. It may be difficult to forecast future events and corresponding impacts and assign probabilities to them. The system may need to be modeled by capturing possible fluctuations within a single scenario or possible correlations between multiple scenarios, which make the analysis further complex.
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24th European Symposium on Computer Aided Process Engineering
Chinedu O. Okoli, Thomas A. AdamsII, in Computer Aided Chemical Engineering, 2014
4.2 Economic results
These results are intuitive, as the feedstock cost makes up a major percentage of the variable operating cost while the mixed alcohols co-product makes up almost half of the products. In order to consider the effects of multiple parameters which change at the same time, different scenarios in which the most significant parameters vary simultaneously are considered as shown in Table 3. The results show that in the “optimistic” to “pessimistic” scenario range of 0.55 — 1.17 $/L the process remains competitive, especially in comparison to ABE-derived butanol prices which range from 0.59 $/L (Kumar et al., 2012) to 1.05 $/L (Qureshi et al., 2013). An important assumption in this study is the alcohol synthesis catalyst CO-conversion of 40 %. This value is in line with the National Renewable Energy Laboratory target of greater than 50 % CO-conversion for alcohol synthesis catalysts, achievable from future research advances (Phillips et al., 2007). However the catalyst used in this study has a CO-conversion of 8.5 % (Herman, 2000), a value that results in an MBSP of 1.60 $/L which is unlikely to be competitive. Furthermore, the base case MBSP result of 0.84 $/L in comparison to the gasoline price of 0.82 $/Lbeq is intuitive, as the MBSP should be worse than the gasoline price except for high crude oil price situations. Thus this process provides a method of producing 2nd generation biofuels at only a small premium over petroleum-derived gasoline, assuming the achievement of the target alcohol synthesis catalyst CO-conversion. In addition, it is important to note that the lignocellulose biomass-to-butanol ABE process has only been demonstrated at the laboratory scale, and thus the wide range of prices for ABE-derived butanol is highly uncertain as it is unlikely to be better than gasoline.
Table 3. Impact of different economic scenarios on the MBSP
| Scenarios – Parameter Values Considered | Very Optimistic | Optimistic | Base Case | Pessimistic | Very Pessimistic |
|---|---|---|---|---|---|
| Internal Rate of Return | 5 | 7.5 | 10 | 12.5 | 15 |
| Feedstock costs ($/dry t) | 60 | 68 | 75 | 83 | 90 |
| Mixed Alcohol value ($/L) | 0.85 | 0.77 | 0.69 | 0.62 | 0.54 |
| Total Depreciable Capital | 230 | 279 | 328 | 377 | 426 |
| Resulting MBSP ($/L) | 0.29 | 0.55 | 0.84 | 1.17 | 1.54 |
Table 2. Breakdown of capital cost
| Capital cost calculations | $ Million |
|---|---|
| Total Installed Equipment | |
| Cost | 214 |
| Total Indirect Cost | 114 |
| Total Depreciable Capital | 328 |
| Royalties | 7 |
| Land | 6 |
| Fixed Capital Investment | 341 |
| Working Capital | 17 |
| Total Capital Investment | 358 |
Figure 2. Sensitivity of the MBSP (base case of 0.84 $/L) to changes in key parameters
Finally, an area to consider in future research is the impact of cheaper feedstocks, such as switchgrass and wheat straw, on the thermochemical biobutanol MBSP. These feedstocks will have different performance metrics, as they will form different syngas compositions with undetermined ripple effects through the process which will have to be evaluated.
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26th European Symposium on Computer Aided Process Engineering
Anvitha Kandiraju, … Ignacio E. Grossmann, in Computer Aided Chemical Engineering, 2016
5 Illustrative Example
The proposed formulation is tested on an a small instance from the air separation industry. This instance has two facilities of the leader and three facilities of the independent suppliers, satisfying demands of 10 markets over a period of 30 quarters (7.5 years). Facilities from the leader have a capacity of 75,000 tons/period and 20,000 tons/period. Independent suppliers have capacities of 100,000 tons/period, 70,000 tons/period and 20,000 tons/period. The capacity expansion limit for each plant is 9000 tons/period. All the capacities are shared between two products with production ratios of 15-25% for product-1 and 75-85% for product-2 with respect to the overall production. There are three demand scenarios that can occur: pessimistic scenario in which the demand drops from 132,700 tons/period to 100,000 tons/period; nominal case where the demand increases from 132,700 tons/period to 161,000 tons/period; and an optimistic case in which the demand further increases from 132,700 tons/period to 256,600 tons/period. Discount rate of 3% is used. Fixed and operating costs increase with time, which are not elaborated here due to space restrictions. We plan the capacity strategy for the leader under the above mentioned conditions.
We consider a stochastic programming model and the equivalent deterministic case in order to compare the performance of the models. The deterministic case is obtained by taking an average over the three demand scenarios. The expansion strategy obtained from the deterministic case is enforced on the stochastic model to analyze the impact of ignoring demand uncertainty. The results are presented in Table 1.
Table 1. Comparison of the results for the deterministic and the stochastic formulations
| Term in objective function | Deterministic solution | Stochastic solution | Deterministic solution under uncertainty |
|---|---|---|---|
| Revenue (MM$): | 1,548 | 1,331 | 1,460 |
| New facilities (MM$): | 0 | 0 | 0 |
| Maintenance (MM$): | 127 | 127 | 127 |
| Expansion (MM$): | 187 | 96 | 187 |
| Production (MM$): | 660 | 568 | 625 |
| Transportation (MM$): | 130 | 120 | 124 |
| NPV (MM$): | 444 | 420 | 397 |
It can be seen from Table1 that the deterministic model gives higher NPV for an average demand case. However, when deterministic solutions are enforced in the face of uncertainties, the model gives lower expected NPV ($397MM) in comparison to the stochastic model ($420MM).
The deterministic model makes aggressive capacity expansion decisions while the stochastic programming model is more conservative. This is because of the high probability of pessimistic and nominal demand scenarios. Considering all demand scenarios in the formulation mitigates the risk associated to demand uncertainties.
The MILP reformulations were implemented in GAMS 24.4.6 and solved using CPLEX 12.6.2.0. The computational statistics for the models are presented in Table 2. The stochastic model has increased size compared to the deterministic case because of the additional scenarios. The increase involves more constraints and continuous variables, while the number of discrete variables remains the same as they are planning decisions which are shared for all scenarios.
Table 2. Model statistics for the proposed formulations
| Model statistic | Deterministic model | Stochastic model |
|---|---|---|
| Number of constraints: | 6,412 | 18,586 |
| Number of continuous variables: | 8,246 | 24,261 |
| Number of binary variables: | 146 | 146 |
| Solution time (s): | 49 | 55 |
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