Optimization of ternary-distillation sequence based on gradient boosting decision tree

doi:10.11949/0438-1157.20230345

Abstract

Abstract:

A machine learning method based on gradient boosting decision tree (GBDT) is proposed for the optimal selection of ternary-distillation process structure. Economic evaluation data of 9 ternary mixtures separated by 7 distillation process structures were used for model training. By applying both traditional decision tree and GBDT models to multiple cases, the results show that the proposed GBDT model has higher prediction accuracy than the traditional decision tree model for both known and unknown ternary mixtures.

Key words: ternary-distillation, optimal distillation configuration, machine learning, gradient boosting decision tree, distillation, computer simulation, optimal design

摘要：

针对三组元精馏流程结构最优选择问题，提出一种基于梯度提升决策树(gradient boosting decision tree，GBDT)的机器学习方法，利用7种精馏流程结构分离9种三组元混合物的经济评价结果数据用于模型训练。分别应用传统决策树和GBDT模型对多个实际案例进行验证，结果显示无论是对已知物系还是未知物系，提出的GBDT模型相较传统决策树模型具有更高的预测准确率。

关键词: 三组元精馏, 精馏最优流程结构, 机器学习, 梯度提升树, 蒸馏, 计算机模拟, 优化设计

CLC Number:

TQ 062.2

Shanghao LIU, Shengkun JIA, Yiqing LUO, Xigang YUAN. Optimization of ternary-distillation sequence based on gradient boosting decision tree[J]. CIESC Journal, 2023, 74(5): 2075-2087.

刘尚豪, 贾胜坤, 罗祎青, 袁希钢. 基于梯度提升决策树的三组元精馏流程结构最优化[J]. 化工学报, 2023, 74(5): 2075-2087.

Figures/Tables 12

Fig.1 Ternary-distillation sequences

Table 1 Ternary mixtures and related parameters

编号	物系(A/B/C)	ESI	$α A B$	$α B C$	$α A C$	meanT_b/K	ΔH/(kJ/mol)
A	异戊烷/正戊烷/正己烷(i-C₅/C₅/C₆）	0.4306	1.2992	3.0174	3.9203	317.3647	79.2729
B	正戊烷/正己烷/正辛烷(C₅/C₆/C₈)	0.4159	2.6132	6.2832	16.4194	349.9767	89.1309
C	苯/甲苯/临二甲苯(B/T/oX)	0.8799	2.3406	2.6601	6.2262	384.8667	100.5916
D	乙醇/丙醇/丁醇(C₂OH/C₃OH/C₄OH)	0.9210	2.0076	2.1798	4.3762	371.2300	123.7694
E	正己烷/正庚烷/正辛烷(C₆/C₇/C₈)	1.0157	2.3264	2.2906	5.3288	370.7633	95.1157
F	正戊烷/正己烷/正庚烷(C₅/C₆/C₇)	1.0251	2.7074	2.6412	7.1510	340.8933	86.2980
G	苯/甲苯/乙苯(B/T/EB)	1.1238	2.3604	2.1004	4.9578	382.1233	99.6300
H	甲苯/乙苯/临二甲苯(T/EB/oX)	1.5840	1.9853	1.2533	2.4882	403.5700	105.4700
I	正戊烷/正庚烷/正辛烷(C₅/C₇/C₈)	2.5696	6.1263	2.3841	14.6059	359.8767	92.0696

Table 1 Ternary mixtures and related parameters

编号	物系(A/B/C)	ESI	$α A B$	$α B C$	$α A C$	meanT_b/K	ΔH/(kJ/mol)
A	异戊烷/正戊烷/正己烷(i-C₅/C₅/C₆）	0.4306	1.2992	3.0174	3.9203	317.3647	79.2729
B	正戊烷/正己烷/正辛烷(C₅/C₆/C₈)	0.4159	2.6132	6.2832	16.4194	349.9767	89.1309
C	苯/甲苯/临二甲苯(B/T/oX)	0.8799	2.3406	2.6601	6.2262	384.8667	100.5916
D	乙醇/丙醇/丁醇(C₂OH/C₃OH/C₄OH)	0.9210	2.0076	2.1798	4.3762	371.2300	123.7694
E	正己烷/正庚烷/正辛烷(C₆/C₇/C₈)	1.0157	2.3264	2.2906	5.3288	370.7633	95.1157
F	正戊烷/正己烷/正庚烷(C₅/C₆/C₇)	1.0251	2.7074	2.6412	7.1510	340.8933	86.2980
G	苯/甲苯/乙苯(B/T/EB)	1.1238	2.3604	2.1004	4.9578	382.1233	99.6300
H	甲苯/乙苯/临二甲苯(T/EB/oX)	1.5840	1.9853	1.2533	2.4882	403.5700	105.4700
I	正戊烷/正庚烷/正辛烷(C₅/C₇/C₈)	2.5696	6.1263	2.3841	14.6059	359.8767	92.0696

Table 2 Product purity for the ternary separations

序号	φ_A	φ_B	φ_C
1	0.95	0.95	0.95
2	0.95	0.99	0.99
3	0.99	0.95	0.99
4	0.99	0.99	0.95
5	0.999	0.95	0.999
6	0.999	0.99	0.95
7	0.999	0.999	0.99
8	0.99	0.99	0.99
9	0.999	0.999	0.999

Fig.2 SVR-assisted selection of optimal distillation sequence and TAC

Fig.3 Rigorously verified short-cut optimization results

Fig.4 Visualization of GBDT subtree (CART tree)

Fig.5 The flowchart of GBDT algorithm

Fig.6 Model prediction accuracy of each mixture as an independent test set

Fig.7 Regionalization of the optimal distillation sequence for mixtures with ESI<1

Table 3 Ternary mixtures and related parameters

物系(A/B/C)	ESI	$α A B$	$α B C$
i-C₄/C₄/i-C₅	0.5173	1.506	2.911
i-C₆/C₆/C₇	0.5069	1.209	2.545
i-C₅/C₅/C₆	0.4306	1.2992	3.0174
C₅/C₆/C₈	0.4159	2.6132	6.2832

Table 3 Ternary mixtures and related parameters

物系(A/B/C)	ESI	$α A B$	$α B C$
i-C₄/C₄/i-C₅	0.5173	1.506	2.911
i-C₆/C₆/C₇	0.5069	1.209	2.545
i-C₅/C₅/C₆	0.4306	1.2992	3.0174
C₅/C₆/C₈	0.4159	2.6132	6.2832

Fig.8 Effect of increasing or decreasing mixtures on i-C5/C5/C6 prediction accuracy

Fig.9 Importance of features

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