Identification of rules for optimal synthesis of ternary-distillation configuration based on decision tree

doi:10.11949/0438-1157.20201929

Abstract

Abstract:

Aiming at the optimized synthesis of the structure of the three-component distillation system, a data-driven classification regression decision tree (CART) model based on information entropy minimization is proposed. Rigorous simulation based optimization results in the literature for ternary distillation configuration synthesis are used to construct data set, which include four ternary mixtures, 34 compositions and seven candidates for distillation configuration. A decision tree trained by using the data and a set of rules for the decision on the optimal ternary distillation configuration is identified. The model test result shows that the classification accuracy of the decision tree model is 88.2%, and the influential factors and the order of their importance are identified.

Key words: ternary-distillation configuration, optimal synthesis, decision tree, Shannon entropy minimization, decision rules

摘要：

针对三组元精馏系统结构的优化合成，提出一种数据驱动的基于信息熵最小化的分类回归决策树（CART）模型。三组元精馏优化数据采用文献中严格模拟优化的结果，数据包含4种三组元混合物、34种进料组成以及7个候选精馏序列结构的最优化设计结果，生成数据集用来训练CART决策树。由决策树训练结果给出了三组元精馏结构最优设计决策规则，模型测试结果显示本文提出的决策树模型对数据集范围内三组元物系精馏结构决策的准确率为88.2%，同时表明了影响三组元最优精馏序列的主要影响因素及其重要性。

关键词: 三组元精馏序列结构, 最优合成, 决策树, 信息熵最小化, 决策规则

CLC Number:

CHEN Xili, SUN Guoming, JIA Shengkun, LUO Yiqing, YUAN Xigang. Identification of rules for optimal synthesis of ternary-distillation configuration based on decision tree[J]. CIESC Journal, 2021, 72(3): 1430-1437.

陈熙理, 孙国铭, 贾胜坤, 罗祎青, 袁希钢. 基于决策树的三组元精馏序列结构最优合成规则识别[J]. 化工学报, 2021, 72(3): 1430-1437.

Figures/Tables 6

Fig.1 Ternary distillation sequences considered in this study

Table 1 Ternary mixtures and separation requirement

物系(A/B/C)	分离纯度要求/%（mol）			GESI
物系(A/B/C)	A	B	C	GESI
异丁烷/正丁烷/异戊烷	0.95	0.95	0.99	0.45
异丁烷/正丁烷/异戊烷	0.99	0.95	0.95	0.65
苯/甲苯/乙苯	0.999	0.956	0.987	0.955
苯/甲苯/乙苯	0.987	0.956	0.999	1.738
正戊烷/正己烷/正庚烷	0.995	0.96	0.989	0.954
正己烷/正庚烷/正辛烷	0.989	0.96	0.982	0.955

Fig.2 A CART decision tree classifier

Fig.3 The visualization of CART for case (a)， the left subtree branch (b) and the right subtree branch (c) of CART

Fig.4 Introduction for node in CART decision tree

Fig.5 Feature importance (FI) measurements on entropy

References 30

1	Tsirlin A M, Balunov A I, Sukin I A. Estimates of energy consumption and selection of optimal distillation sequence for multicomponent distillation[J]. Theoretical Foundations of Chemical Engineering, 2016, 50(3): 250-259.
2	Mirko S. Fast screening of energy and cost efficient intensified distillation processes[J]. Chemical Engineering Transactions, 2018, 69(1): 199-204.
3	Pleşu V, Bonet Ruiz A E, Bonet J, et al. Shortcut assessment of alternative distillation sequence schemes for process intensification[J]. Computers & Chemical Engineering, 2015, 83(5): 58-71.
4	Glinos K, Malone M F. Minimum reflux, product distribution, and lumping rules for multicomponent distillation[J]. Industrial & Engineering Chemistry Process Design and Development, 1984, 23(4): 764-768.
5	Glinos K, Malone M F. Minimum vapor flows in a distillation column with a sidestream stripper[J]. Industrial & Engineering Chemistry Process Design and Development, 1985, 24(4): 1087-1090.
6	Glinos K N, Nikolaides I P, Malone M F. New complex column arrangements for ideal distillation[J]. Industrial & Engineering Chemistry Process Design and Development, 1986, 25(3): 694-699.
7	Takase H, Hasebe S. Synthesis of ternary distillation process structures featuring minimum utility cost using the IDEAS approach[J]. AIChE Journal, 2018, 64(4): 1285-1294.
8	田芳, 袁野, 袁希钢, 等.物系和分离要求对最优三组元精馏结构选择的影响[J].化工学报, 2017, 68(2): 708-715.
	Tian F, Yuan Y, Yuan X G, et al. Influence of mixture and separation requirements on optimal configuration for ternary distillation[J]. CIESC Journal, 2017, 68(2): 708-715.
9	朱怀工, 王燕, 张敏卿.进料性质对立式隔板塔操作特性的影响[J].化工进展, 2009, 28(4): 579-583.
	Zhu H G, Wang Y, Zhang M Q. Influence of feed property on the operation of dividing wall column[J]. Chemical Industry and Engineering Progress, 2009, 28(4): 579-583.
10	Tedder D W, Rudd D F. Parametric studies in industrial distillation(Part I): Design comparisons[J]. AIChE Journal, 1978, 24(2): 303-315.
11	Agrawal R, Fidkowski Z T. Are thermally coupled distillation columns always thermodynamically more efficient for ternary distillations?[J]. Industrial & Engineering Chemistry Research, 1998, 37(8): 3444-3454.
12	Wang L, Tian F, Yuan X, et al. A generalized ease of separation index for selection of optimal configuration of ternary distillation[J]. Chemical Engineering Transactions, 2018, 69(1): 241-246.
13	Lin W, Huang P, Ward J D. Regions of optimality for separation system synthesis using rigorous modeling, stochastic optimization, and column stacking[J]. Industrial & Engineering Chemistry Research, 2020, 59(26): 12164-12175.
14	袁野. 分离三组分物系精馏方式的模拟和优化选择[D]. 天津: 天津大学, 2015.
	Yuan Y. Simulation and optimal selection of distillation schemes for separating ternary mixtures[D]. Tianjin: Tianjin University, 2015.
15	王磊. 三组元最优精馏结构筛选的通用分离因子与定量化规则[D]. 天津: 天津大学, 2019.
	Wang L. Generalized ease of separation index and quantitative rules for selection of optimal ternary-distillation configuration[D]. Tianjin: Tianjin University, 2019.
16	Quinlan J R. Induction of decision trees[J]. Machine Learning, 1986, 1(1): 81-106.
17	De'Ath G, Fabricius K E. Classification and regression trees: a powerful yet simple technique for ecological data analysis[J]. Ecology, 2000, 81(11): 3178-3192.
18	Wankat P C. Multieffect distillation processes[J]. Industrial & Engineering Chemistry Research, 1993, 32(5): 894-905.
19	Douglas J M. Conceptual Design of Chemical Processes[M]. New York: McGraw-Hill Book Company, 1988: 30-216.
20	Kotsiantis S B. Decision trees: a recent overview[J]. Artificial Intelligence Review, 2013, 39(4): 261-283.
21	Song Y Y, Lu Y. Decision tree methods: applications for classification and prediction[J]. Shanghai Archives of Psychiatry, 2015, 27(2): 130-135.
22	Podgorelec V, Zorman M. Decision tree learning[M]//Encyclopedia of Complexity and Systems Science. Berlin: Springer, 2015.
23	Nisbet R, Miner G, Yale K. Perface[M]//Handbook of Statistical Analysis and Data Mining Applications. 2nd ed. Canada: Academic Press, 2017: 822.
24	Gordon A D, Breiman L, Friedman J H, et al. Classification and regression trees[J]. Biometrics, 1984, 40(3): 874
25	Shannon C E. A mathematical theory of communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423.
26	Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in python[J]. Journal of Machine Learning Research, 2011, 12(85): 2825-2830.
27	Ellson J, Gansner E R, Koutsofios E, et al. Graphviz and Dynagraph - Static and Dynamic Graph Drawing Tools[M]. Heidelberg: Springer, 2004: 127-148.
28	陆恩锡, 吕向红. 热耦蒸馏及其选用原则[J]. 化学工程, 2005, 33(2): 9-12.
	Lu E X, Lyu X H. Thermally coupled distillation and its selection principles[J]. Chemical Engineering(China), 2005, 33(2): 9-12
29	Fonyó Z, Rév E, Szitkai Z, et al. Energy savings of integrated and coupled distillation systems[J]. Computers & Chemical Engineering, 1999, 23: S89-S92.
30	Rév E, Emtir M, Szitkai Z, et al. Energy savings of integrated and coupled distillation systems[J]. Computers & Chemical Engineering, 2001, 25(1): 119-140.

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