Dealing with Risk in Development Projects for Chemical Products and Processes

Abstract

Decision-making and optimization approaches in chemical product/process design often incorporate probability distributions to express uncertainty about the underlying data. We will show in this paper that probabilities are subjective, that they are caused by the decision makers' lack of confidence in the underlying knowledge, and that the shape of the distribution is affected by psychological input factors. Furthermore, distributions do not necessarily express the range of possible values but rather express the values that are perceived to be possible. Decisions based on distributions can, therefore, be wrong. Screening literature, however, reveals that the origin of risk and uncertainty is not fully understood and should be discussed. To close this gap, we explain where risk, distributions, and probabilities come from. On the basis of these insights, we provide a new optimization framework that describes a process of systematically identifying the optimum alternative by stepwise resolving uncertainty. Finally, we apply the optimization methodology on a case study.

Techno-economic Assessment Framework for the Chemical Industry—Based on Technology Readiness Levels

“…Assessment in RD&D deals with the evaluation of economic activity that will be conducted in the future and is therefore -in contrast to evaluating past activities -naturally afflicted with uncertainty. 27 In the course of an RD&D process the availability of data increases, allowing for the choice of methods that consider more data and inversely reduce uncertainty. In order to minimize risk, maintain competitiveness and reduce RD&D cycle times, it is crucial to select methods that best utilize available data.…”

“…Following the interpretation, decision-making is again a very complex process and includes portfolio strategy considerations and other assessment fields. , A positive indication given by the interpretation of a profitability indicator does not necessarily imply a positive decision as the error allowances of deciders may differ. Companies’ decision rules vary from a very conservative approach that the intervals for estimated selling price and estimates cost must not overlap to a more risk-seeking approach being to opt for an investment even if only cost plus highest error and selling price minus lowest error match.…”

Life cycle thinking and general modelling contribution to chemical process sustainable design and operation

“…According to Granger et al (1990, Ch 4. p50), empirical parameters (or chance variables) are the only ones that are susceptible of description by a probabilistic measure a probability distribution function (pdf, based on frequentist or Bayesian analysis 54 .). Bode et al (2007) link the frequentist approach to objective probabilities, where the realisation probability of an outcome A (p f (A)) is defined as the limit of actual realisations of A (n f A) divided by the total number of experiments (n f ) as in Eq. 2.36.…”

“…These variables are the only type of quantity that are uncertain and can be said to have a true value as opposed to appropriate or good values that are subject of bias due to value decision. This definition of true and good values rises only from the way these pdfs were generated, Bode et al (2007) argue that using measurement data does not mean that the probabilities are objective, data may differ between scientists, trust on data will be different and may be rejected, consequently distributions derived from experimentation are subjective and will depend on the scientist knowledge (trust/experience) and consequently will always be Bayesian.…”

“…These concepts of distinguishing alternatives under uncertainty are embedded in the stochastic-dominance principle. This principle, as described by Bode et al (2007), is based on the assumption that all alternatives are correlated. Consequently a change in the input values' realisations would cause all alternative's outputs values to make the same shift.…”