Reaction solvent design method based on Dragon descriptors and modified decision tree-genetic algorithm

doi:10.11949/j.issn.0438-1157.20181049

Abstract

Abstract:

Reaction solvents have been widely used in liquid-liquid homogeneous organic synthesis. They have significant impacts on reaction rates and selectivity, which have contributed to the development of new process route for green synthesis. A computer-aided molecular design (CAMD) reaction solvent design method based on Dragon descriptor and SMILES (simplified molecular-input line-entry system) coding is proposed. First, a reaction kinetic model was constructed to make quantitative predictions for reaction rate constants k by the decision tree-genetic algorithm (DT-GA). Then, through SMILES code techniques and Dragon software, computer-aided molecular design (CAMD) method was integrated with the DT-GA to establish a mixed integer nonlinear programming (MINLP) model consists of objective functions and constraint equations. Afterwards, a decomposition-based algorithm was employed to solve this MINLP optimization problem, which achieves the objective of reaction solvent design. Finally, an example of Diels-Alder reaction was adapted to demonstrate the feasibility and effectiveness of this method.

Key words: systems engineering, solvents, algorithm, optimization, reaction

摘要：

反应溶剂被广泛应用于液-液均相有机合成中，能够大幅度提高反应速率与选择性，有助于绿色合成新工艺路线的开发。提出了一种基于Dragon描述符与SMILES (simplified molecular-input line-entry system)编码的计算机辅助(computer-aided molecular design, CAMD)反应溶剂设计方法。首先，利用决策树-遗传算法构建可定量预测反应速率常数k的反应动力学模型；基于构建的反应动力学模型，提出了集成决策树-遗传算法与CAMD设计方法，通过SMILES分子编码算法生成同分异构体，并利用Dragon软件计算描述符大小，建立由目标函数与约束方程组成的混合整数非线性规划(mixed integer nonlinear programming, MINLP)模型，进一步采用分解算法对模型进行优化求解，从而实现反应溶剂设计目标；最后，以Diels-Alder反应为例，验证了该方法的可行性与有效性。

关键词: 系统工程, 溶剂, 算法, 优化, 反应

CLC Number:

TQ 413.2

Qilei LIU, Kun FENG, Linlin LIU, Jian DU, Qingwei MENG, Lei ZHANG. Reaction solvent design method based on Dragon descriptors and modified decision tree-genetic algorithm[J]. CIESC Journal, 2019, 70(2): 533-540.

刘奇磊, 冯锟, 刘琳琳, 都健, 孟庆伟, 张磊. 基于Dragon描述符与改进的决策树-遗传算法的反应溶剂设计方法[J]. 化工学报, 2019, 70(2): 533-540.

Figures/Tables 9

Fig.1 Principle of DT-GA method

Fig.2 CAMD design method integrated with DT-GA

Table 1 GC method and solvent property constraints

溶剂性质	基团贡献法公式	上/下限
T _m/K	$T m = 144.0977 l n (∑ n i ∈ G n i T m, i)$	$T m ≤ 303.15$
T _b/K	$T b = 244.7889 l n (∑ n i ∈ G n i T b, i)$	$T b ≥ 303.15$
S/MPa^1/2	$S = 20.7339 + ∑ n i ∈ G n i S i$	$18.05 ≤ S ≤ 22.73$
LC₅₀ FM/(mol·L^-1)	$- l n L C 50 F M = ∑ n i ∈ G n i L C 50, i$	$- l n L C 50 F M ≤ 3.30$

Table 1 GC method and solvent property constraints

溶剂性质	基团贡献法公式	上/下限
T _m/K	$T m = 144.0977 l n (∑ n i ∈ G n i T m, i)$	$T m ≤ 303.15$
T _b/K	$T b = 244.7889 l n (∑ n i ∈ G n i T b, i)$	$T b ≥ 303.15$
S/MPa^1/2	$S = 20.7339 + ∑ n i ∈ G n i S i$	$18.05 ≤ S ≤ 22.73$
LC₅₀ FM/(mol·L^-1)	$- l n L C 50 F M = ∑ n i ∈ G n i L C 50, i$	$- l n L C 50 F M ≤ 3.30$

Fig.3 Principle of decomposition-based algorithm

Fig.4 Chemical equation of Diels-Alder reaction between cyclopentadiene and 2-propenal

Fig.5 Determination coefficients R 2 of 8 types descriptors

Table 2 Regression data of reaction kinetic model

溶剂	lgk	GATS5p	ATS3e	ATSC3s	ATS3v	ATSC1s	ATSC2p
乙酸	?2.491	0.000	2.370	45.125	0.948	15.896	0.498
乙醇	?2.964	0.000	2.548	13.642	0.868	5.025	0.554
1-丙醇	?3.186	0.000	3.050	11.757	1.505	3.910	1.145
1-丁醇	?3.219	0.211	3.382	9.000	1.922	3.000	1.723
甲醇	?3.257	0.000	1.298	3.000	0.189	4.000	0.113
三氯甲烷	?3.383	0.000	0.000	0.000	0.000	8.747	0.564
1,2-二氯乙烷	?3.602	0.000	2.390	16.111	1.286	3.396	1.608
二甲基甲酰胺	?3.640	0.000	2.824	3.667	1.811	1.917	0.879
二氯甲烷	?3.699	0.000	0.000	0.000	0.000	5.254	1.101
甲苯	?3.745	0.625	3.277	6.296	2.400	3.580	2.364
乙腈	?3.757	0.000	1.453	14.062	0.469	1.937	0.460
1,4-二氧六环	?3.827	0.000	3.381	14.000	2.039	0.000	1.528
四氯化碳	?3.873	0.000	0.000	0.000	0.000	5.239	0.014
丙酮	?3.983	0.000	2.650	27.387	1.311	1.773	1.001
乙酸乙酯	?4.036	0.954	3.126	27.500	1.849	3.166	1.283

Fig.6 Predicted lgk and experimental lgk

Table 3 Results of reaction solvents design

Predicted lgk	SMILES	分子结构
?2.269	CCC(Cl)CC(O)COC(C) O
?2.350	CC(CCCC(C)O)C(O)C[N ⁺](O)[O ^?]
?2.413	CCCC(C(O)CC)C(Cl)C(C) O
?2.422	CCCCC(C(C)O)C(O)C[N ⁺](O)[O ^?]

References 30

1	Struebing H , Ganase Z , Karamertzanis P G , et al . Computer-aided molecular design of solvents for accelerated reaction kinetics[J]. Nature Chemistry, 2013, 5(11): 952-957.
2	Otto R , Brox J , Trippel S , et al . Single solvent molecules can affect the dynamics of substitution reactions[J]. Nature Chemistry, 2012, 4(7): 534-538.
3	Jiménez-González C , Poechlauer P , Broxterman Q B , et al . Key green engineering research areas for sustainable manufacturing: a perspective from pharmaceutical and fine chemicals manufacturers[J]. Organic Process Research & Development, 2011, 15(4): 900-911.
4	Zhang L , Babi D K , Gani R . New vistas in chemical product and process design[J]. Annu. Rev. Chem. Biomol. Eng., 2016, 7(1): 557-582.
5	Brignole E A , Bottini S , Gani R . A strategy for the design and selection of solvents for separation processes[J]. Fluid Phase Equilibria, 1986, 29 (29): 125-132.
6	Gani R , Nielsen B , Fredenslund A . A group contribution approach to computer‐aided molecular design[J]. AIChE J., 1991, 37 (9): 1318-1332.
7	Harper P M , Gani R . A multi-step and multi-level approach for computer aided molecular design[J]. Computers & Chemical Engineering, 2001, 24(2): 677-683.
8	Zhang L , Cignitti S , Gani R . Generic mathematical programming formulation and solution for computer-aided molecular design[J]. Computers & Chemical Engineering, 2015, 78: 79-84.
9	Giovanoglou A , Barlatier J , Adjiman C S , et al . Optimal solvent design for batch separation based on economic performance[J]. AIChE J., 2003, 49(12): 3095-3109.
10	Achenie L E K . Computer Aided Molecular Design: Theory and Practice[M]//Gani R, Venkatasubramanian V. Netherlands: Elsevier, Academic Press, 2003: 9-10.
11	Gani R , Gómez P A , Folić M , et al . Solvents in organic synthesis: replacement and multi-step reaction systems[J]. Computers & Chemical Engineering, 2008, 32(10): 2420-2444.
12	Folić M , Adjiman C S , Pistikopoulos E N . Design of solvents for optimal reaction rate constants[J]. AIChE J., 2007, 53(5): 1240-1256.
13	Folić M , Adjiman C S , Pistikopoulos E N . Computer-aided solvent design for reactions: maximizing product formation[J]. Industrial & Engineering Chemistry Research, 2008, 47(15): 5190-5202.
14	Zhou T , Lyu Z , Qi Z , et al . Robust design of optimal solvents for chemical reactions—a combined experimental and computational strategy[J]. Chemical Engineering Science, 2015, 137(2): 613-625.
15	Datta S , Dev V A , Eden M R . Hybrid genetic algorithm-decision tree approach for rate constant prediction using structures of reactants and solvent for Diels-Alder reaction[J]. Computers & Chemical Engineering, 2017, 106(2): 690-698.
16	Liu Q , Zhang L , Liu L , et al . GC-COSMO based reaction solvent design with new kinetic model using CAMD[C]//Eden M R, Ierapetritou M G, Towler G. 13th International Symposium On Process Systems Engineering. Netherlands: Elsevier, Academic Press, 2018: 235-240.
17	Gani R , Jiménez-González C , Constable D J C . Method for selection of solvents for promotion of organic reactions[J]. Computers & Chemical Engineering, 2005, 29(7): 1661-1676.
18	Carlson R . Design and Optimization in Organic Synthesis[M]//Carlson J E. 2nd ed. Netherlands: Elsevier, Academic Press, 2005: 232-240.
19	Kamlet M J , Taft R W . The solvatochromic comparison method(1): The β-scale of solvent hydrogen-bond acceptor(HBA) basicities[J]. J. Am. Chem. Soc., 1976, 98(2): 377-383.
20	Taft R W , Abboud J L M , Kamlet M J , et al . Linear solvation energy relations[J]. Journal of Solution Chemistry, 1985, 14(3): 153-186.
21	Mauri A , Consonni V , Pavan M , et al . Dragon software: an easy approach to molecular descriptor calculations[J]. Match, 2006, 56(2): 237-248.
22	Odele O , Macchietto S . Computer aided molecular design: a novel method for optimal solvent selection[J]. Fluid Phase Equilibria, 1993, 82(93): 47-54.
23	Fredenslund A . Vapor-liquid Equilibria Using Unifac. A Group-Contribution Method[M]//Gmehling J, Rasmussen P. Netherlands: Elsevier, Academic Press, 1977: 156-160.
24	Gmehling J , Li J , Schiller M . A modified UNIFAC model(2): Present parameter matrix and results for different thermodynamic properties[J]. Industrial & Engineering Chemistry Research, 1993, 32(1): 178-193.
25	Wittig R , Lohmann J , Gmehling J . Vapor-liquid equilibria by UNIFAC group contribution(6): Revision and extension[J]. Industrial & Engineering Chemistry Research, 2003, 42(1): 183-188.
26	Hukkerikar A S . Development of pure component property models for chemical product-process design and analysis[D]. Denmark: Technical University of Denmark, 2013.
27	Karunanithi A T , Achenie L E K , Gani R . A new decomposition-based computer-aided molecular/mixture design methodology for the design of optimal solvents and solvent mixtures[J]. Industrial & Engineering Chemistry Research, 2005, 44(13): 4785-4797.
28	Cativiela C , García J I , Mayoral J A , et al . Solvent effects on endo/exo- and regio-selectivities of Diels-Alder reactions of carbonyl-containing dienophiles[J]. Cheminform, 1994, 25(32): 847-851.
29	Cativiela C , García J I , Mayoral J A , et al . Modelling of solvent effects on the Diels-Alder reaction[J]. Chemical Society Reviews, 1996, 25(3): 209-218.
30	Nobuoka K , Kitaoka S , Yanagisako A , et al . Stereoselectivity of the Diels-Alder reaction in ionic liquids with cyano moieties: effect of the charge delocalization of anions on the relation of solvent-solvent and solute–solvent interactions[J]. RSC Advances, 2013, 3(42): 19632-19638.

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