A simple heuristic method for the systematic synthesis of initial sequences for multicomponent separations is proposed and applied to a number of synthesis problems which have been solved previously using other methods. Based on reported costs, it is shown that the initial sequences synthesized for the test problems by the new heuristic method are cheaper than those obtained by other ordered heuristic methods. These initial sequences are also either identical to or at most a few percents higher in costs than those optimum sequences obtained by other algorithmic, heuristic-algorithmic and heuristicevolutionary methods. The new method is straightforward to apply by hand and it does not require any mathematical background and computational skill from the user. SCOPEAn important process design problem is the systematic synthesis of multicomponent separation sequences which is concerned with the systematic selection of the method and sequence for separating a multicomponent mixture into several products of relatively pure species. The general techniques which have been developed for solving the separation sequencing problem have included: algorithmic approaches involving some established optimization principles (e.g., Hendry and Hughes, 1972); heuristic methods based on the use of rules of thumb (e.g., Rudd et al., 1973, pp. 155-208); evolutionary strategies wherein improvements are systematically made to an initially created separation sequence (e.g., Stephanopoulos and Westerberg, 1976); and thermodynamic methods involving applications of thermodynamic principles (e.g., Hohmann et al., 1980). In some situations, two or more of these techniques have been used together in the synthesis (e.g., Seader and Westerberg, 1977). A review of previous studies on multicomponent separation sequencing can be found in Nishida et al. (1981).A disadvantage of many existing algorithmic and evolutionary techniques as reviewed by Nishida et al. is that their applications require special mathematical background and computational skill from the user. Although heuristic rules to guide the order of separation sequencing have long been available, many of the known heuristics contradict or overlap others; and procedures to resolve these conflicts have not been adequately developed. Recently, some success has been reported on the applications of certain heuristics together with evolutionary strategies for multicomponent separation sequencing (Seader and Westerberg, 1977; Nath and Motard, 1981).In this work, a simple heuristic method for the systematic synthesis of initial sequences for multicomponent separations is proposed. A comparison of the new heuristic method with other recent methods, particularly the heuristicevolutionary methods by Seader and Westerberg (1977) and by Nath and Motard (1981), is presented. A number of illustrative examples are given to demonstrate the simplicity and effectiveness of the proposed method. CONCLUSIONS AND SIGNIFICANCEThis work proposes and demonstrates a simple heuristic method for the systematic synthes...
There is industrial incentive to extract aromatics from ethylene cracker feeds, but the conventional sulfolane solvent was found not economical by Meindersma and coworkers. Ionic liquids (ILs) have long been considered alternative aromatic extraction solvents. This work develops energy‐optimum aromatic extraction processes for an ethylene cracker feed using IL solvents. We avoid pitfalls of using simplified feeds and a priori thermodynamic property estimates, with the largest set of experimentally regressed UNIQUAC binary parameters for the IL, 1‐ethyl‐3‐methylimidazolium bis([trifluoromethyl]sulfonyl)imide ([EMIM][NTf2]). We screen process energy and operating conditions for [EMIM][NTf2] and sulfolane at varying aromatic feed contents and find [EMIM][NTf2] favorable at low aromatic feed contents. Adding light and heavy components of the ethylene cracker feed necessitates process modifications. Our novel steam‐assisted extractive distillation developed for [EMIM][NTf2] is also suitable for sulfolane. We show that the [EMIM][NTf2] solvent can reduce 10.7% of energy consumption compared to sulfolane using the same novel process.
This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.
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