Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is...
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Identifying sustainable chemical processes often depends on the choice of enabling materials that directly influence the overall performance. Matching property targets while incorporating adequate process knowledge is essential for optimal material selection. Multi-scale decisions need to be taken simultaneously to determine the optimal process configurations, operating conditions, and material structures. Integrating molecular to process scale decisions within an equation-oriented optimization framework leads to large-scale mixed-integer nonlinear programs (MINLP). Over the years, several solution approaches have been suggested to tackle this issue. Here, the current state-of-the-art in the field of computer-aided molecular and process design (CAMPD) is discussed and key challenges and open questions are highlighted that may stimulate future research.
The search for improved CO2 capture solvents can be accelerated by deploying computer-aided molecular and process design (CAMPD) techniques to explore large molecular and process domains systematically. However, the d...
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The search for improved CO2 capture solvents can be accelerated by deploying computer-aided molecular and process design (CAMPD) techniques to explore large molecular and process domains systematically. However, the direct solution of the integrated molecular-processdesign problem is very challenging as nonlinear interactions between physical properties and process performance render a large proportion of the search space infeasible. We develop a methodology that enables the direct and reliable solution of CAMPD for absorption- desorption processes, using the state-of-the-art SAFT-gamma Mie group contribution approach to predict phase and chemical equilibria. We develop new feasibility tests and show them to be highly efficient at reducing the search space, integrating them in an outer-approximation algorithm. The framework is applied to design an aqueous solvent and CO2 chemical absorption-desorption process, with 150 CAMPD instances across three case studies solved successfully. The optimal solvents are more promising than those obtained with sequential moleculardesign approaches.
The performance of Organic Rankine cycle (ORC) systems is defined by the system design as well as working fluid selection. Integrated thermo-economic optimisation of both can unlock maximum system potential in terms o...
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The performance of Organic Rankine cycle (ORC) systems is defined by the system design as well as working fluid selection. Integrated thermo-economic optimisation of both can unlock maximum system potential in terms of power generation at a minimal cost. However, such optimisation is associated with uncertainties related to the underlying thermodynamic fluid models, ORC system models, and equipment cost correlations. In this paper, the main sources of uncertainty are quantified and their impact on optimal system design and working fluid selection is analysed. A computer-aided molecular and process design (CAMPD) optimisation framework based on first-law system design models is developed and validated with experimental data. Results reveal that the developed framework can identify promising working fluid candidates with high probabilities, even considering the most important sources of uncertainty. In a case study of industrial waste-heat utilisation, it was found that while uncertainties challenge the strict discrimination of the most promising working fluids, they mainly affect absolute performance values, rather than the overall ranking of working fluids. Propane was identified as having a 94-% probability of being among the best 3 working fluids. Furthermore, although the overall specific investment costs are highly uncertain (mean: 3810 pound/kW, standard deviation: 720 pound/kW), the results are less sensitive to uncertainties in fluid equilibrium and transport properties (standard deviation: 160 pound/kW), with the impact of equipment cost uncertainties being dominant. The analysis of uncertainties in working fluid selection also applies to other CAMPD problems, and other applications of group-contribution-based equations of state.
The need to consider multiple objectives in moleculardesign, whether based on techno-economic, environmental or health and safety metrics is increasingly recognized. There is, however, limited understanding of the su...
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The need to consider multiple objectives in moleculardesign, whether based on techno-economic, environmental or health and safety metrics is increasingly recognized. There is, however, limited understanding of the suitability of different multi-objective optimization (MOO) algorithm for the solution of such design problems. In this work, we present a systematic comparison of the performance of five mixed-integer non-linear programming (MINLP) MOO algorithms on the selection of computer-aidedmoleculardesign (CAMD) and computer-aided molecular and process design (CAMPD) problems. The five methods are designed to address the discrete and nonlinear nature of the problem, with the aim of generating an accurate approximation of the Pareto front. They include: a weighted sum approach without global search phases (SWS), a weighted sum approach with simulated annealing (WSSA), a weighted sum approach with multi level single linkage (WSML), the sandwich algorithm with MLSL (SDML) and the non dominated sorting genetic algorithm-II (NSGA-II). The algorithms are compared systematically in two steps. The effectiveness of the global search methods is evaluated with SWS, WSSA and WSML. WSML is found to be most effective and a comparative analysis of WSML, SDML and NSGA-II is then undertaken. As a test set of these optimization techniques, two CAMD and one CAMPD problems of varying dimensionality are formulated as case studies. The results show that the SDML provides the most efficient generation of a diverse set of Pareto points, leading to the construction of an approximate Pareto front close to exact Pareto front. (C) 2020 Elsevier Ltd. All rights reserved.
molecular-level decisions are increasingly recognized as an integral part of processdesign. Finding the optimal process performance requires the integrated optimization of process and solvent chemical structure, lead...
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molecular-level decisions are increasingly recognized as an integral part of processdesign. Finding the optimal process performance requires the integrated optimization of process and solvent chemical structure, leading to a challenging mixed-integer nonlinear programming (MINLP) problem. The formulation of such problems when using a group contribution version of the statistical associating fluid theory, SAFT- Mie, to predict the physical properties of the relevant mixtures reliably over process conditions is presented. To solve the challenging MINLP, a novel hierarchical methodology for integrated process and solvent design (hierarchical optimization) is presented. Reduced models of the process units are developed and used to generate a set of initial guesses for the MINLP solution. The methodology is applied to the design of a physical absorption process to separate carbon dioxide from methane, using a broad selection of ethers as the moleculardesign space. The solvents with best process performance are found to be poly(oxymethylene)dimethylethers. (c) 2015 American Institute of Chemical Engineers AIChE J, 61: 3249-3269, 2015
As property and process models with many variables need to be considered, integrated computer-aided molecular and process design (CAMPD) problems are computationally expensive. An efficient CAMPD approach is proposed ...
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As property and process models with many variables need to be considered, integrated computer-aided molecular and process design (CAMPD) problems are computationally expensive. An efficient CAMPD approach is proposed for the simultaneous design of solvents and extractive distillation (ED) processes based on a data-driven modeling strategy. First, artificial neural network (ANN)-based process models are trained to replace the physical models conventionally used in CAMPD. Subsequently, optimization is performed to maximize process performance, through which optimal solvent properties and corresponding optimal process parameters are obtained. Then, real solvents approximating the optimal property values are identified from a large solvent database. Rigorous simulations of the ED process are performed to evaluate the performance of the optimal solvents and corresponding process parameters. Further economic evaluation (6.11% lower annual cost compared to the benchmark process) and chemical hazard assessment confirm that acetylacetone is a promising solvent for the ED separation of 1-butene from 1,3-butadiene.
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