大型聚酯生产过程智能建模、控制与优化研究
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摘要
聚酯(聚对苯二甲酸乙二酯)是一种广泛应用于生产和日常生活的高分子聚合物,其生产过程具有高度非线性、慢时变性及分布参数的特点。随着聚酯产品市场竞争的加剧,聚酯工业生产过程的优化运行在提高企业效益方面的优越性逐渐体现出来。以支持向量机、高斯过程、进化算法等为代表的智能方法已在化工领域得到了应用,解决了复杂化工系统的控制与优化问题。然而,高斯过程作为一种统计建模方法,当样本增加时,其参数计算复杂性增长很快,难以用于工业现场实际；以分布估计算法为研究热点的进化算法在优化过程中,面临着如何更好地估计进化过程中的概率模型、跳出局部最优等问题。因此,智能方法在化工领域的广泛应用仍需要进一步研究和拓展。
鉴此,本文围绕大型聚酯生产过程的工业实际问题,应用智能方法,对其建模、控制与优化的若干理论和技术进行研究,开发了聚酯过程的智能建模技术、预测控制与智能优化技术,形成了具有实际应用价值的聚酯生产过程优化运行软件,为聚酯生产过程优化提供了新方法和新技术,包括：
针对分布估计算法求解连续优化问题时数据分布概率模型不易确定的问题,提出了基于核密度估计的单目标和多目标分布估计算法,讨论了算法中核宽度的选择准则,通过数值仿真验证了算法的有效性。该类算法被用于聚酯过程反应动力学参数的优化问题,获得了符合工业装置实际操作工况的工业聚酯过程模型,进而实现了基于工艺机理的聚酯生产过程全流程模拟与工业验证。
研究了聚酯原料乙二醇生产过程的智能建模问题,采用先验知识与支持向量机的融合方法,提出了表达输入变量单调性的支持向量机模型,用于乙二醇生产氧化反应过程的催化剂失活建模,实现了氧化反应过程的智能机理建模。采集工业装置运行数据,对乙二醇水合反应过程的模型参数进行了优化,获得了符合工业装置实际操作工况的水合反应动力学模型,实现了SD技术的乙二醇生产过程全流程模拟与工业验证。
针对聚酯过程波动、干扰以及条件的变化对时间的累积效应问题,分别研究了聚酯生产酯化过程和终缩聚过程的动态建模问题。对酯化过程建立了集中参数动态模型,分析了端羧基浓度、气相乙二醇流量等对进料摩尔比、温度、压力等的阶跃响应动态特性,得到了酯化段反应器、工艺塔相互作用下系统的动态变化规律。采用多反应器串连的方式建立了终缩聚反应器的动态模型,分析了特性粘度对真空度、温度等的阶跃响应动态特性,得到了终缩聚过程的动态变化规律。
研究了高斯过程的软测量建模方法。针对大样本导致高斯过程计算复杂度增加的问题,提出了基于聚类的稀疏高斯过程方法,建立了聚酯酯化反应过程的端羧基浓度软测量模型,降低了模型的计算复杂性,得到了模型预测结果及均方差。研究了拟似输入稀疏高斯过程,增加了在线校正方法,建立了机理不明确的聚酯产品色值模型。研究了基于高斯过程的非线性系统预测控制方法,对聚酯终缩聚过程的特性粘度进行了预测控制仿真。
基于对聚酯过程运行优化的需要,提出了分布估计算法与柯西分布、粒子群算法相结合的混合智能优化算法,以聚酯生产过程的能量消耗最小为目标,对聚酯过程工艺机理模型进行了过程优化,找出了装置最优工作点,实现了工业过程的节能。
基于面向服务和多智能体框架,对聚酯生产过程的建模、控制和优化的研究成果进行了集成,建立了工业过程优化运行系统框架。详细讨论了流程行业信息系统中基本服务的定义,服务之间的交互,搭建了基于WEB SERVICE的过程优化运行平台,开发了大型聚酯生产过程的建模、控制与优化的运行优化集成平台。
Poly (Ethylene-Terephthalate) (PET) is a kind of polymers which is widely used in industrial process and daily life. Production process of PET is highly non-linear, slow time-varying and with distributed parameters. With severe competition in PET market, profit improvements of running optimization in industrial processes attract much attention. Artificial intelligence methods such as support vector machines, Gaussian processes and evolutionary algorithms have been applied in the domain of chemical process engineering. These methods solved some control and optimization problems of complex chemical system. However, as a statistic method, optimizations of Gaussian process parameters have complex computation; estimation of distribution algorithm, a hot spot of evolutionary algorithm, is faced with the selection of probability model and easiness to trap into local optimals. The wide application of intelligence methods still needs further research and development.
This paper focuses on industrial problems of large-scale PET production process and emphasizes on the theoretical and technical research on applying artificial intelligence method to modelling, control and optimization. Running optimization software of PET process has been developed. All of these provide a new way for PET process operation optimization. This paper covers the following parts:
For the problem that probability model of population is hard to be determined during evolution of estimation of distribution algorithm, single and multi-objective kernel density estimation of distribution algorithms were proposed. The selection criterial of kernel width has also been discussed. Numerical simulation results validated the effectiveness of the algorithms. The algorithms were applied to optimize kinetic parameters of PET process with plant operating data. The industrial kinetic model has been obtained and its efficiency were validated via comparing with industrial data. Based on mechanism, whole process model of PET was developed and certificated with industrial data.
In this paper intelligent modeling method of ethylene glycol which is raw material of PET was discussed. Priori knowledge was used into support vector machine and support vector model with input variables monotone was proposed. This method was used for modeling of catalyst deactivation and intelligence modeling of oxidation reactor was implemented. Kinetic parameters were optimized with plant data and model of hydration reactor was developed. Whole process of ethylene glycol were simulated and validated with industrial data.
In order to find cumulative effection of process fluctuations, disturbance and mutative conditions, dynamic model of esterification and final polycondensation process were developed. Step responses of end carboxyl concentration and ethylene flow rate to feed molar ratio, temperature, pressure were discussed and dynamic characteristics of esterification process were obtained. Dynamic model of polycondensation process was established using series reactors and step responses of intrinsic viscosity to vacuum, temperature were analyzed.
This paper discussed gaussian process for soft sensor modeling. Clustering based sparse gaussian process was proposed in order to reduce the complex computation of gaussian process. It was used in soft sensor modeling of end carboxyle concentration in esterification process and the model gave the prediction and mean variance. Pseudo-input sparse gaussian process was used in this paper and online learning method was added. It is used in the modeling of b value of PET. Also gaussian process was also used in prediction control. Prediction control based on gaussian process was used for the control of instrinsic viscosity in final polycondensation process.
For the optimization of PET process, hybrid optimization algorithms based on estimation of distribution algorithm, Cauchy distribution and particle swarm optimization were proposed. Based on the whole process model of PET, energy consumption was optimized using hybrid optimization algorithm. The optimal operation were obtained and used in industrial process for energy-saving.
Based on service-oriented architecture and multi-agent, modeling, control and optimization were integrated into running optimization framework. Basic services in industry information system were defined and interactions between services were discussed. The platform of running optimization was developed and integration software of PET modeling, control and optimization were established.
鉴此,本文围绕大型聚酯生产过程的工业实际问题,应用智能方法,对其建模、控制与优化的若干理论和技术进行研究,开发了聚酯过程的智能建模技术、预测控制与智能优化技术,形成了具有实际应用价值的聚酯生产过程优化运行软件,为聚酯生产过程优化提供了新方法和新技术,包括：
针对分布估计算法求解连续优化问题时数据分布概率模型不易确定的问题,提出了基于核密度估计的单目标和多目标分布估计算法,讨论了算法中核宽度的选择准则,通过数值仿真验证了算法的有效性。该类算法被用于聚酯过程反应动力学参数的优化问题,获得了符合工业装置实际操作工况的工业聚酯过程模型,进而实现了基于工艺机理的聚酯生产过程全流程模拟与工业验证。
研究了聚酯原料乙二醇生产过程的智能建模问题,采用先验知识与支持向量机的融合方法,提出了表达输入变量单调性的支持向量机模型,用于乙二醇生产氧化反应过程的催化剂失活建模,实现了氧化反应过程的智能机理建模。采集工业装置运行数据,对乙二醇水合反应过程的模型参数进行了优化,获得了符合工业装置实际操作工况的水合反应动力学模型,实现了SD技术的乙二醇生产过程全流程模拟与工业验证。
针对聚酯过程波动、干扰以及条件的变化对时间的累积效应问题,分别研究了聚酯生产酯化过程和终缩聚过程的动态建模问题。对酯化过程建立了集中参数动态模型,分析了端羧基浓度、气相乙二醇流量等对进料摩尔比、温度、压力等的阶跃响应动态特性,得到了酯化段反应器、工艺塔相互作用下系统的动态变化规律。采用多反应器串连的方式建立了终缩聚反应器的动态模型,分析了特性粘度对真空度、温度等的阶跃响应动态特性,得到了终缩聚过程的动态变化规律。
研究了高斯过程的软测量建模方法。针对大样本导致高斯过程计算复杂度增加的问题,提出了基于聚类的稀疏高斯过程方法,建立了聚酯酯化反应过程的端羧基浓度软测量模型,降低了模型的计算复杂性,得到了模型预测结果及均方差。研究了拟似输入稀疏高斯过程,增加了在线校正方法,建立了机理不明确的聚酯产品色值模型。研究了基于高斯过程的非线性系统预测控制方法,对聚酯终缩聚过程的特性粘度进行了预测控制仿真。
基于对聚酯过程运行优化的需要,提出了分布估计算法与柯西分布、粒子群算法相结合的混合智能优化算法,以聚酯生产过程的能量消耗最小为目标,对聚酯过程工艺机理模型进行了过程优化,找出了装置最优工作点,实现了工业过程的节能。
基于面向服务和多智能体框架,对聚酯生产过程的建模、控制和优化的研究成果进行了集成,建立了工业过程优化运行系统框架。详细讨论了流程行业信息系统中基本服务的定义,服务之间的交互,搭建了基于WEB SERVICE的过程优化运行平台,开发了大型聚酯生产过程的建模、控制与优化的运行优化集成平台。
Poly (Ethylene-Terephthalate) (PET) is a kind of polymers which is widely used in industrial process and daily life. Production process of PET is highly non-linear, slow time-varying and with distributed parameters. With severe competition in PET market, profit improvements of running optimization in industrial processes attract much attention. Artificial intelligence methods such as support vector machines, Gaussian processes and evolutionary algorithms have been applied in the domain of chemical process engineering. These methods solved some control and optimization problems of complex chemical system. However, as a statistic method, optimizations of Gaussian process parameters have complex computation; estimation of distribution algorithm, a hot spot of evolutionary algorithm, is faced with the selection of probability model and easiness to trap into local optimals. The wide application of intelligence methods still needs further research and development.
This paper focuses on industrial problems of large-scale PET production process and emphasizes on the theoretical and technical research on applying artificial intelligence method to modelling, control and optimization. Running optimization software of PET process has been developed. All of these provide a new way for PET process operation optimization. This paper covers the following parts:
For the problem that probability model of population is hard to be determined during evolution of estimation of distribution algorithm, single and multi-objective kernel density estimation of distribution algorithms were proposed. The selection criterial of kernel width has also been discussed. Numerical simulation results validated the effectiveness of the algorithms. The algorithms were applied to optimize kinetic parameters of PET process with plant operating data. The industrial kinetic model has been obtained and its efficiency were validated via comparing with industrial data. Based on mechanism, whole process model of PET was developed and certificated with industrial data.
In this paper intelligent modeling method of ethylene glycol which is raw material of PET was discussed. Priori knowledge was used into support vector machine and support vector model with input variables monotone was proposed. This method was used for modeling of catalyst deactivation and intelligence modeling of oxidation reactor was implemented. Kinetic parameters were optimized with plant data and model of hydration reactor was developed. Whole process of ethylene glycol were simulated and validated with industrial data.
In order to find cumulative effection of process fluctuations, disturbance and mutative conditions, dynamic model of esterification and final polycondensation process were developed. Step responses of end carboxyl concentration and ethylene flow rate to feed molar ratio, temperature, pressure were discussed and dynamic characteristics of esterification process were obtained. Dynamic model of polycondensation process was established using series reactors and step responses of intrinsic viscosity to vacuum, temperature were analyzed.
This paper discussed gaussian process for soft sensor modeling. Clustering based sparse gaussian process was proposed in order to reduce the complex computation of gaussian process. It was used in soft sensor modeling of end carboxyle concentration in esterification process and the model gave the prediction and mean variance. Pseudo-input sparse gaussian process was used in this paper and online learning method was added. It is used in the modeling of b value of PET. Also gaussian process was also used in prediction control. Prediction control based on gaussian process was used for the control of instrinsic viscosity in final polycondensation process.
For the optimization of PET process, hybrid optimization algorithms based on estimation of distribution algorithm, Cauchy distribution and particle swarm optimization were proposed. Based on the whole process model of PET, energy consumption was optimized using hybrid optimization algorithm. The optimal operation were obtained and used in industrial process for energy-saving.
Based on service-oriented architecture and multi-agent, modeling, control and optimization were integrated into running optimization framework. Basic services in industry information system were defined and interactions between services were discussed. The platform of running optimization was developed and integration software of PET modeling, control and optimization were established.
引文
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