化工学报 ›› 2020, Vol. 71 ›› Issue (S1): 282-292.doi: 10.11949/0438-1157.20190795

• 过程系统工程 • 上一篇    下一篇

基于分子表面电荷密度分布与机器学习的混合物设计方法研究

毛海涛1(),王璐1,许志颖2,解万翠2,都健1,张磊1()   

  1. 1.大连理工大学化工学院化工系统工程研究所,辽宁 大连 116024
    2.青岛科技大学海洋科学与生物工程学院,山东 青岛 266042
  • 收稿日期:2019-07-10 修回日期:2019-09-13 出版日期:2020-04-25 发布日期:2020-05-22
  • 通讯作者: 张磊 E-mail:haitaomao0730@foxmail.com;keleiz@dlut.edu.cn
  • 作者简介:毛海涛(1994—),男,硕士研究生,haitaomao0730@foxmail.com
  • 基金资助:
    国家自然科学基金项目(21808025)

Mixture product design based on molecular surface charge density distribution and machine learning

Haitao MAO1(),Lu WANG1,Zhiying XU2,Wancui XIE2,Jian DU1,Lei ZHANG1()   

  1. 1.Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, Liaoning, China
    2.College of Marine Science and Biological Engineering, Qingdao University of Science & Technology, Qingdao 266042, Shandong, China
  • Received:2019-07-10 Revised:2019-09-13 Online:2020-04-25 Published:2020-05-22
  • Contact: Lei ZHANG E-mail:haitaomao0730@foxmail.com;keleiz@dlut.edu.cn

摘要:

由于混合物性能的可调控性,当前市场对其关注与日俱增。对于这类产品,基于模型的设计方法由于具有高效性以及普适性,相较于其他产品设计方法得到了更快的发展。但是对于很多性质,如气味、颜色等,准确且普适的模型尚不可得。因此,本文提出了一种基于分子表面电荷密度分布描述符(S描述符)和机器学习模型的混合物设计方法,采用描述符表征产品、再通过机器学习模型将其与性质关联,直接用于混合物产品设计。具体地,根据给定的产品性质需求,机器学习模型直接预测/设计混合物产品的S描述符;然后以欧几里德距离为指标,在给定的数据库中筛选出S描述符满足要求的候选混合物组成。最后,对候选混合物及其组分性质进行实验验证,完成设计。本文以香精的混合替代物设计作为算例,设计得到丙酸叶醇酯的两种混合香精替代物,通过实验对混合物进行了验证。结果表明,混合替代物的气味及其组分的各理化性质均与丙酸叶醇酯相近,证实本文所提出方法的有效性。

关键词: 系统工程, 产品设计, 神经网络, 分子表面电荷密度分布, 混合物设计, 香精

Abstract:

Modern business pays increasing attentions to mixture products due to its adjustable characteristics. For the design methods towards such kinds of products, the development of model-based design methods is faster than others, because of its efficiency and wide application. However, the models for some properties, like odor and color, with acceptable accuracy or general application range are still not available. Therefore, an application methodology of machine learning (ML) with molecular surface charge density distribution (Sdescriptors) for mixture product design is proposed in this study, where descriptors are employed to represent the product and ML is responsible for correlating them to the target properties, for the purpose of designing product directly. Specifically, machine learning model is expected to predict Sdescriptors of candidate products according to the assigned property value, and ingredients are screened out using Euclidean-based method according to the predicted descriptors. Finally, the properties of the candidate mixtures and its ingredients are verified by experiments. This methodology is introduced using a case study of mixture substitution fragrance design for cis-3-hexenyl propionate and two mixture fragrances are obtained ultimately. The odor properties of mixtures and physicochemical properties of their components are similar to the target, which highlights the effective of the proposed method.

Key words: systems engineering, product design, neural network, molecular surface charge density distribution, mixture design, fragrance

中图分类号: 

  • TQ 021.8

图1

分子表面电荷密度分布谱图以及对其极性区域积分得到的S描述符"

图2

机器学习模型的建立流程"

图3

混合香精设计流程"

表1

基团贡献法以及阈值"

组分性质基团贡献法公式阈值
δ/MPa1/2δ=1000H298Vap-RTV298m12δ158.9
Tb/KTb=244.7889lniGniTb,iTb333
Tf/KTf=150.0218+iGniTf,iTf323
Ko/wlnKo/w=0.4876+iGniKo/w,ilnKo/w2.91
LC50/(mol·L-1)-lnLC50=2.18+iGniLC50,i-lnLC503.70

表2

PEN3电子鼻的检测对象以及检出限"

传感器名称检测气味类型

检出限/

(mg/kg)

W1C有机化合物10
W5S氮氧化物1
W3C氨类10
W6S氢气0.1
W5C烷烃与非极性有机化合物1
W1S甲烷100
W1W含硫有机化合物1
W2S酒精100
W2W无机硫1
W3S有机化合物以及脂肪族有机化合物10

图4

基于10折交叉验证法建立的IML模型的平均验证性能与平均测试性能"

图5

IML模型在24个测试样本上的预测性能"

表3

丙酸叶醇酯的物性需求"

参数数值参数数值
可食用32.42馊味25.93
烘焙味43.00愉悦度60.19
甜味34.37蒸气压/Pa53.86
水果味31.54扩散系数/(m2/h)0.16
花香味29.95

图6

IML模型预测结果与Keller数据库中组合的替代混合物之间的S描述符欧氏距离差"

表4

丙酸叶醇酯替代香精的设计结果"

参数丙酸叶醇酯替代混合物1替代混合物2
4-异丙基苯甲醇左旋香芹酮混合物偏差2-甲基戊酸2-乙基丁酸烯丙酯混合物偏差
CAS No.33467-74-2536-60-76485-40-197-61-07493-69-8
体积分数10.20.80.40.6
溶解度δ(298 K,水)/(mg/L)158.91687367.1472.1815000157.35935.48
沸点Tb(101.325 kPa) /K453-455512.66499.4747.11-49.11468.36449.962.32-4.32
闪点Tf/K333498.15465.15138.75364.26327.599.258
Ko/w2.9092.372.71-0.2671.82.972-0.4058
LC50/(mol·L-1)3.363.253.390.0022.454.030.038

图7

不同温度下丙酸叶醇酯及其替代混合物组分的蒸气压与扩散系数"

表5

丙酸叶醇酯替代混合物的测试实验设备以及试剂"

试剂说明
丙酸叶醇酯北京迈瑞达科技有限公司,纯度>98%
2-甲基戊酸北京迈瑞达科技有限公司,纯度>98%
2-乙基丁酸烯丙酯北京迈瑞达科技有限公司,纯度>97%
4-异丙基苯甲醇河南郑州阿尔法化工有限公司,纯度>99%
左旋香芹酮河南郑州阿尔法化工有限公司,纯度>97%
95%乙醇天津市富宇化工有限公司

图8

丙酸叶醇酯以及两组混合香精替代物的电子鼻检测雷达图"

1 Rodríguez O, Gomes P, Mata V, et al. Chapter 1 - A Product Engineering Approach in the Perfume Industry [M]//Teixeira M A. Perfume Engineering. Oxford: Butterworth-Heinemann, 2019: 1-13.
2 Wibowo C, Ng K M. Product-oriented process synthesis and development: creams and pastes[J]. AIChE Journal, 2001, 47(12): 2746-2767.
3 Fung K Y, Ng K M. Product-centered processing: pharmaceutical tablets and capsules[J]. AIChE Journal, 2003, 49(5): 1193-1215.
4 Gani R, Brignole E A. Molecular design of solvents for liquid extraction based on UNIFAC[J]. Fluid Phase Equilibria, 1983, 13(83): 331-340.
5 Joback K G. Designing molecules possessing desired physical property values[D]. Massachusetts: Massachusetts Institute of Technology, 1989
6 Conte E, Gani R, Ng K M. Design of formulated products: a systematic methodology[J]. AIChE Journal, 2011, 57: 2431-2449.
7 Kontogeorgis G M, Michele M, Ng K M, et al. An integrated approach for the design of emulsified products[J]. AIChE Journal, 2019, 65: 75-86.
8 张磊, 刘琳琳, 都健. 替代燃油的计算机辅助设计方法[J]. 化工进展, 2018, 37(6): 2438-2444.
Zhang L, Liu L L, Du J. A computer-aided design methodology for tailor-made surrogate fuels[J]. Chemical Industry and Engineering Progress, 2018, 37(6): 2438-2444.
9 Hornic K. Multilayer feedforward networks are universal approximators[J]. Neural Networks, 1989, 2: 359-366.
10 Raccuglia P, Elbert K C, Adler P D, et al. Machine-learning-assisted materials discovery using failed experiments[J]. Nature, 2016, 533: 73-76.
11 苏荣欣, 邹龙花, 齐崴, 等. 酪蛋白-胰酶水解历程分子量变化模拟与三维表征[J]. 化工学报, 2013, 64(1): 346-351.
Su R X, Zou L H, Qi W, et al. Simulation and 3D plot of molecular weight distribution of released peptides from pancreatic hydrolysis of casein[J]. CIESC Journal, 2013, 64(1): 346-351.
12 黄凯, 陈勇, 母志为, 等. 基于人工神经网络和遗传算法的甲烷制氢催化剂设计[J]. 化工学报, 2016, 67(8): 3481-3490.
Huang K, Chen Y, Mu Z W, et al. Catalyst design for production of hydrogen from methane based on artificial neural network and genetic algorithm[J]. CIESC Journal, 2016, 67(8): 3481-3490.
13 安爱民, 刘云利, 张浩琛, 等. 微生物燃料电池的动态性能分析及其神经网络预测控制[J]. 化工学报, 2017, 68(3): 1090-1098.
An A M, Liu Y L, Zhang H C, et al. Dynamic performance analysis and neural network predictive control of microbial fuel cell[J]. CIESC Journal, 2017, 68(3): 1090-1098.
14 林生岭, 徐绍芬, 王俊德, 等. 钙钛矿型LaxSr1-xNi1-yCoyO3光电催化活性研究[J]. 化学学报, 2005, 63(5): 385-390.
Lin S L, Xu S F, Wang J D, et al. Study on photo-electro catalytic activity of perovskite type oxides LaxSr1-xNi1-yCoyO3[J]. Acta Chimica Sinica, 2005, 63(5): 385-390.
15 Zhang L, Mao H, Liu L, et al. A machine learning based computer-aided molecular design/screening methodology for fragrance molecules[J]. Computers & Chemical Engineering, 2018, 115: 295-308.
16 Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter engineering[J]. Science, 2018, 361: 360-365.
17 Klamt A, Schueuermann G J. COSMO: a new approach to dielectric screening in solvents with explicit expressions for the screening energy and its gradient[J]. Journal of the Chemical Society, Perkin Transactions II, 1993, 5: 799-805.
18 Rossiter K J. Structure-odor relationships[J]. Chemical Review, 1996, 96: 3201-3240.
19 Klamt A, Reinisch J, Eckert F, et al. Polarization charge densities provide a predictive quantification of hydrogen bond energies[J]. Physical Chemistry Chemical Physics, 2011, 14(2): 955-963.
20 Lin S T, Sandler S I. A priori phase equilibrium prediction from a segment contribution solvation model[J]. Industrial & Engineering Chemistry Research, 2002, 41(5): 899-913.
21 Klamt A, Eckert F, Arlt W. COSMO-RS: an alternative to simulation for calculating thermodynamic properties of liquid mixtures[J]. Annual Review of Chemical and Biomolecular Engineering, 2010, 1: 101-122.
22 Kang X, Liu X, Li J, et al. Heat capacity prediction of ionic liquids based on quantum chemistry descriptors[J]. Industrial & Engineering Chemistry Research, 2018, 57(49): 16989-16994.
23 Kang X, Zhao Z, Qian J, et al. Predicting the viscosity of ionic liquids by the ELM intelligence algorithm[J]. Industrial & Engineering Chemistry Research, 2017, 56(39): 11344-11351.
24 Palomart J, Torrecilla J S, Ferro V R, et al. Development of an a priori ionic liquid design tool (Ⅱ): Ionic liquid selection through the prediction of COSMO-RS molecular descriptor by inverse neural network[J]. Industrial & Engineering Chemistry Research, 2009, 48(4): 2257-2265.
25 Keller A, Vosshall L B. Olfactory perception of chemically diverse molecules[J]. BMC Neuroscience, 2016, 17: 55.
26 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016: 23-53.
Zhou Z H. Machine Learning[M]. Beijing: Tsinghua University Press, 2016: 23-53.
27 刘方, 徐龙, 马晓迅. BP神经网络的发展及其在化学化工中的应用[J]. 化工进展, 2019, 38(6): 2559-2573.
Liu F, Xu L, Ma X X. Development of BP neural network and its application in chemistry and chemical engineering[J]. Chemical Industry and Engineering Progress, 2019, 38(6): 2559-2573.
28 de Bruyne M, Foster K, Carlson J R. Odor coding in the Drosophila antenna[J]. Neuron, 2001, 30(2): 537-552.
29 Tamir A. In Applications of Markov Chains in Chemical Engineering[M]. Amsterdam, Netherlands: Elsevier, 1998.
30 Marrero J, Gani R. Group-contribution based estimation of pure component properties[J]. Fluid Phase Equilibria, 2001, 183: 183-208.
31 Hukkerikar A S. Development of pure component property models for chemical product-process design and analysis[D]. Denmark: Technical University of Denmark, 2013.
32 马琦, 伯继芳, 冯莉, 等. GC-MS结合电子鼻分析干燥方式对杏鲍菇挥发性风味成分的影响[J]. 食品科学, 2019, 40(14): 276-282.
Ma Q, Bo J F, Feng L, et al. Effect of drying method on volatile components of pleurotus eryngii analyzed by combined use of GC-MS and electronic nose[J]. Food Science, 2019, 40(14): 276-282.
33 Reid R C, Prausnitz J M, Poling B E. The Properties of Gases & Liquids[M]. New York: McGrawHill, 1988.
34 Lee B I, Kesler M. A generalized thermodynamic correlation based on three-parameter corresponding states[J]. AIChE Journal, 1975, 21: 510-527.
[1] 贺鹏程, 庄莉, 胡亮, 刘刚, 王瑞琪, 包亚强. 板翅式换热器压力特性工程计算方法[J]. 化工学报, 2020, 71(S1): 172-178.
[2] 方黄峰, 刘瑶瑶, 张文彪. 基于LSTM神经网络的流化床干燥器内生物质颗粒湿度预测[J]. 化工学报, 2020, 71(S1): 307-314.
[3] 贺彦林, 田业, 顾祥柏, 徐圆, 朱群雄. 基于正则化的函数连接神经网络研究及其复杂化工过程建模应用[J]. 化工学报, 2020, 71(3): 1072-1079.
[4] 张璐, 张嘉成, 韩红桂, 乔俊飞. 基于模糊神经网络的污水处理生化除磷过程控制[J]. 化工学报, 2020, 71(3): 1217-1225.
[5] 李晨莹, 刘琳琳, 张磊, 顾偲雯, 都健. 不确定性下基于多工况优化的可控性换热器网络综合[J]. 化工学报, 2020, 71(3): 1154-1162.
[6] 黄正梁, 王超, 李少硕, 杨遥, 孙婧元, 王靖岱, 阳永荣. 基于深度学习的气液固三相反应器图像分析方法及应用[J]. 化工学报, 2020, 71(1): 274-282.
[7] 张楠, 陈龙祥, 胡芃. 混合工质临界性质的推算研究[J]. 化工学报, 2019, 70(S2): 1-7.
[8] 曹晨鑫, 杜玉鹏, 王昕, 王振雷. 基于Ms-LWPLS的化工过程网络化性能分级评估方法[J]. 化工学报, 2019, 70(S1): 141-149.
[9] 陈虎, 陈倩, 刘长军, 黄卡玛, 龙卓. 基于SIW的介电系数宽带测量装置[J]. 化工学报, 2019, 70(S1): 182-185.
[10] 王羽鹏, 梁俊伟, 罗向龙, 李逸帆, 陈健勇, 陈颖. 基于神经网络的有机朗肯循环过程及循环性能计算方法[J]. 化工学报, 2019, 70(9): 3256-3266.
[11] 廉小亲, 王俐伟, 安飒, 魏伟, 刘载文. 基于SOM-RBF神经网络的COD软测量方法[J]. 化工学报, 2019, 70(9): 3465-3472.
[12] 柴伟, 郭龙航, 池彬彬. 污水处理厂出水水质变量区间预测建模[J]. 化工学报, 2019, 70(9): 3449-3457.
[13] 章聪, 江锦波, 彭旭东, 赵文静, 李纪云. 近临界区CO2物性预测模型对比与修正[J]. 化工学报, 2019, 70(8): 3058-3070.
[14] 乔俊飞, 贺增增, 杜胜利. 基于混合评价指标的自组织模糊神经网络设计研究[J]. 化工学报, 2019, 70(7): 2606-2615.
[15] 王志甄, 邹志云. 基于神经网络的pH中和过程非线性预测控制[J]. 化工学报, 2019, 70(2): 678-686.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] 韩进, 朱彤, 今井刚, 谢里阳, 徐成海, 野崎勉. 基于高速转盘法的剩余污泥可溶化处理 [J]. 化工学报, 2008, 59(2): 478 -483 .
[2] 王晓莲, 王淑莹, 彭永臻. 进水C/P比对A2/O工艺性能的影响 [J]. 化工学报, 2005, 56(9): 1765 -1770 .
[3] 罗雄麟, 白玉杰, 侯本权, 孙琳. 基于相对增益分析的换热网络旁路设计 [J]. 化工学报, 2011, 62(5): 1318 -1325 .
[4] 唐志杰, 唐朝晖, 朱红求. 一种基于多模型融合软测量建模方法 [J]. 化工学报, 2011, 62(8): 2248 -2252 .
[5] 张建文, 李亚超, 陈建峰. 旋转床内微观混合与反应过程的特性[J]. 化工学报, 2011, 62(10): 2726 -2732 .
[6] 杨基础,董燊,杨小民. 海藻糖对固定化酶的保护作用 [J]. CIESC Journal, 2000, 51(2): 193 -197 .
[7] 梁运涛, 曾文. 封闭空间瓦斯爆炸与抑制机理的反应动力学模拟 [J]. 化工学报, 2009, 60(7): 1700 -1706 .
[8] 魏清渤,高楼军,付 峰,张玉琦,马荣萱. pH响应PAAm-g-PEG/PVP半互穿网络水凝胶的制备以及溶胀动力学[J]. 化工进展, 2012, 31(01 ): 163 -168 .
[9] 赵亚红,薛振华,王喜明,王丽. 羧甲基纤维素/蒙脱土纳米复合材料对刚果红染料的吸附及解吸性能[J]. 化工学报, 2012, 63(8): 2655 -2660 .
[10] 汪泽华,蔡卫权,郭蕾,童亚超,胡玉珍. P123辅助SB粉溶胶制备大孔径介孔γ-Al2O3及其对甲基蓝的强化吸附性能[J]. 化工学报, 2012, 63(8): 2623 -2628 .