Decision support tools for environmentally benign process design under uncertainty (original) (raw)
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Quality costs and robustness criteria in chemical process design optimization
Computers & Chemical Engineering, 2001
The identification and incorporation of quality costs and robustness criteria is becoming a critical issue while addressing chemical process design problems under uncertainty. This article presents a systematic design framework that includes Taguchi loss functions and other robustness criteria within a single-level stochastic optimization formulation, with expected values in the presence of uncertainty being estimated by an efficient cubature technique. The solution obtained defines an optimal design, together with a robust operating policy that maximizes average process performance. Two process engineering examples (synthesis and design of a separation system and design of a reactor and heat exchanger plant) illustrate the potential of the proposed design framework. Different quality cost models and robustness criteria are considered, and their influence in the nature and location of best designs systematically studied. This analysis reinforces the need for carefully considering/addressing process quality and robustness related criteria while performing chemical process plant design.
Incorporating uncertainty in chemical process design for environmental risk assessment
Environmental Progress, 2004
The effects of uncertainty in thermophysical properties on the evaluation of the environmental performance is demonstrated with a chemical process to recover toluene and ethyl acetate by absorption from a gaseous waste stream of a cellophane production plant. In this case study, the environmental performance is defined as the estimation of the volatile organic compounds (VOCs) and total emissions of the plant and of several environmental risk indexes. We found that estimations of VOCs are very sensitive to uncertainty in thermophysical properties such as infinite-dilution activity coefficients, and vapor pressures (through uncertain temperature variations). Additionally, we concluded that calculation of the total emissions can be very sensitive to fuel content factors such as those used to estimate greenhouse gases. This can have such an impact on the emission calculations that a detailed model of the given chemical process might not be required for the estimation of the total emissions. In other words, a simpler process flowsheet model can perform the same task just as well, with the results within the variations caused by uncertainty in the thermophysical properties. We demonstrate a Monte Carlo approach that allows the detection of such uncertainty characteristics in a design, providing a rational basis for prediction of the associated environmental risks. This procedure also enables the deconvolution of various sources of uncertainty, and the estimation of physical property uncertainty through a similarity approach. We concluded that our framework can be used to enhance decision making by uncovering uncertainties and sensitivities in chemical process simulation. © 2004 American Institute of Chemical Engineers Environ Prog, 2004
Uncertainty in chemical process systems engineering: a critical review
Reviews in Chemical Engineering, 2019
Uncertainty or error occurs as a result of a lack or misuse of knowledge about specific topics or situations. In this review, we recall the differences between error and uncertainty briefly, first, and then their probable sources. Then, their identifications and management in chemical process design, optimization, control, and fault detection and diagnosis are illustrated. Furthermore, because of the large amount of information that can be obtained in modern plants, accurate analysis and evaluation of those pieces of information have undeniable effects on the uncertainty in the system. Moreover, the origins of uncertainty and error in simulation and modeling are also presented. We show that in a multidisciplinary modeling approach, every single step can be a potential source of uncertainty, which can merge into each other and generate unreliable results. In addition, some uncertainty analysis and evaluation methods are briefly presented. Finally, guidelines for future research are proposed based on existing research gaps, which we believe will pave the way to innovative process designs based on more reliable, efficient, and feasible optimum planning.
Decision support tools for process design and selection
2001
This chapter discusses a new tool that is capable of reducing the complexity of the process synthesis problem and analyzing a tradeoff among the environmental impact, economy, and robustness of the process. New efficient process-robustness parameters are also proposed. Controllability is indicated by the failure probability that can be calculated with a small number of iterations. The operability is evaluated by the deviation ratio. Applicability to the method is illustrated in a case study. Three types of closed-loop toluene recovery processes––membrane-based, condensation-based, and adsorption-based––are investigated to quantitatively compare the characteristics of each process. By the proposed methodology, it is possible to design an appropriate process with minimal environmental impact and maximal robustness at a desired economic performance.
Environmentally conscious design of chemical processes and products: Multi-optimization method
Chemical Engineering Research and Design, 2009
This paper presents an environmentally conscious integrated methodology for design and optimization of chemical process especially for separation process, whose energy consumption occupies more than 70% of the whole process. The methodology incorporates environmental factors into the chemical process synthesis at the initial design stage, which is totally different with the traditional end-of-pipe treatment method. Firstly, one rigorous model for simulation of multi-stages and multi-components separation process was developed, and based on our proposed environmental impact assessment method, the calculation methods of the reasonable economic and environment objective are constructed. Then one multi-objective mixed integer non-linear mathematical model was established by considering environmental and economic factors. Finally, the high non-linear model was solved by multi-objective evolutionary algorithm (non-dominated sorting genetic algorithm). It is often difficult to find an optimum for a process that satisfies both economic and environmental objectives simultaneously. Normally, an arrangement of optimal solutions is obtained, which forms a non-inferior set. Identifying the optimum from this non-inferior set is subjective, depending on the preference of decision makers. In this paper, technique for order preference by similarity to ideal solution (TOPSIS) for identifying the set of optimal parameters is developed and used at the decision-making step, in which the preference relation for the decision-maker over the objectives is adopted by trade-off information between objectives. The proposed methodology was highlighted through two industrialized processes, dimethyl carbonate production processes by pressure-swing distillation and extraction distillation process, respectively.
AIChE Journal, 2014
One of the major tasks of structural engineering design optimization is the handling of uncertainties (such as variations in material properties, loading conditions, unknown environmental conditions or even uncertainties in modeling assumptions), which affect system performance in terms of robustness and reliability (or, in other words, the ability to respond to input variations with minimal alteration, loss of functionality or damage). This task is usually tackled with Optimization Under Uncertainty (OUU) methods[1], like robust design optimization and reliability-based design optimization. In most cases, the optimization has to deal with multi-objective problems (such as maximizing the performance while minimizing costs, system response variations, etc). These problems do not have a unique solution, but a set of tradeoff optimal solutions (the so-called Pareto front). The action of a decision maker (DM) is necessary for choosing the final optimal design according to some (pre-defined) preferences or criteria. Multi-Criteria Decision Making (MCDM) techniques[2] have been developed over the past years to try to make these choices objective and rational. In most MCDM methods, the preferences are usually taken into account during some a-posteriori analyses of the optimization outcomes. Here we address both OUU and MCDM problems with an approach that integrates directly the action of the DM with the optimization process. The DM is asked to express their preferences (based on their previous experience) to drive the optimization towards the most preferred regions of the Pareto front. This can lead to a more efficient exploration of specific regions of the Pareto front and reduce the computational cost of finding desirable solutions. Interactive MCDM approaches have been recently given more attention in the multi-objective optimization community [3, 4, 5]. A validation of this approach on simple test-cases is shown as well as its application to the design of a simple building structure under uncertainties with seismic hazard and snow loads.
Competition requires companies to make decisions that satisfy multiple criteria. Considering profitability alone is no longer sufficient. Ignoring environmental considerations will not only expose a company to potential regulatory costs, but also damaged public image, both of which in turn have negative effect on the economic well-being of companies. At the same time, the fast changing business environment requires companies to reach decisions in a speedy fashion. This work describes a decision-making framework that addresses the obstacles in integrating environmental considerations into technology selections with focus on the semiconductor and flat panel industry. It addresses data availability and data quality issues in environmental evaluations through the uncertainty analysis. It tackles the mismatch between the short innovation cycles in the industry and the long environmental analysis time by a combination of the uncertainty analysis, non-linear sensitivity analysis, hierarchical modeling, and the value of information analysis. It bridges the gap between environmental evaluations, economical evaluations, and technical evaluations by a unified modeling platform that links the process model, the cost-of-ownership model, and the environmental valuation model along with the databases and the random number generators for the uncertainty analysis. It is a generic framework and can be applied to various decision scenarios that face uncertainty in their systems. The paper also reviews sensitivity analysis methods and includes a survey on the current status and needs on environmental, safety, and health in the industry. A case study on Cu CVD illustrates the methods of the evaluations models. A case study on comparing NF3 and F2 as the chamber cleaning gas illustrates the decision-making framework.
Optimal Process Design with Model Parameter Uncertainty and Process Variability
Optimal design under unknown information is a key task in process systems engineering. This study considers formulations that incorporate two types of unknown input parameters, uncertain model parameters, and ®ariable process parameters. In the former case, a process must be designed that is feasible o®er the entire domain of uncertain parameters, while in the latter case, control ®ariables can be adjusted during process operation to compensate for ®ariable process parameters. To address this problem we extend the two-stage formulation for design under uncertainty and deri®e new formulations for the multiperiod and feasibility problems. Moreo®er, to simplify the feasibility problem in the two-stage algorithm, we also introduce a KS constraint aggregation function and deri®e a single, smooth nonlinear program that approximates the feasibility problem. Three case studies are presented to demonstrate the proposed approach.