Multi-component reboiled absorber modeling and parametric studies

The steady state operation of an industrial multi-feed, multi-component reboiled absorber column which was previously investigated by Petryschuk (1964) has been simulated by two alternate mathematical models — an equilibrium stage model and a tray efficiency model based on the Murphree tray efficiency concept. Development of the models required the use of a set of physical property estimation procedures which differed from those used by the previous investigator. When subjected to parametric variation studies, both new models successfully reproduce known quantitative and qualitative behavior attributed to the column. The study results demonstrate not only the sensitivity of a simulation of a multicomponent separation process upon physical property estimation procedures, but also indicates the difficulty in model discrimination when plant data of only limited range and accuracy is available.     

Dynamic synergistic modelling for cobalt removal process in zinc hydrometallurgy and the research of parameter estimation based on unscented Kalman filter

The cobalt removal process with arsenic salt of zinc hydrometallurgy has serious non-linearity, uncertainty, and mutual coupling. Its accurate dynamic modelling has always been a challenging problem. On the basis of in-depth analysis of cobalt removal process and reaction mechanism, considering the cascade relationship between the reactors, a dynamic synergistic continuously stirred tank reactor (SCSTR) mechanism model of the cobalt removal process was constructed. Aiming at the unknown parameters in the SCSTR model, the idea of the Kalman filter was introduced, and the unknown parameters were characterized as unknown states; a method of estimating the unknown model parameters was developed using the augmented state equation and the unscented Kalman filter (UKF) algorithm. Simulation results with industrial data of a zinc smeltery showed that the parameter estimation model has high accuracy, and the estimated parameters can be used in the SCSTR model. An intensive simulation analysis of the dynamic characteristics of the complete SCSTR model was carried out to verify the influence of different input disturbances on the output ion concentration of each reactor, which demonstrated the excellent dynamic performance and potential of the model. Ultimately, according to the industrial calculation analysis, the SCSTR model has a guiding effect on the addition of zinc powder in the reactors, overcomes the blindness in the production process, and provides a momentous basis for the optimization control of the cobalt removal process.     

Risk assessment and drawing information system based change management

Most process changes and modifications, which are caused by the process failure, the process life cycle and the economic environment, have been generated at industrial facilities. However, management of change (MOC) is based on basic process safety, such as change judgment, risk check with accurate technical references and risk assessment, and is difficult to carry out because of the lack of experience, knowledge, and process safety specialists. In this study, the MOC system was developed based on process safety technology and the drawing information system (DIS) oriented method. This study recommends the MOC system to industrial facilities as the setup of the obvious standard for the decision-making process, the MOC procedure based risk assessment, and the judgment and risk estimation of the process modification. The HIT and CAT modules were developed using the risk assessment checklist, HAZOP risk ranking, scenarios for consequence analysis and reporting automation. These two modules provide a clearcut view on the process risk. An effective risk-based MOC Review was performed by this new method. Additionally, this study suggests a new method for the MOC S/W system. This method was developed using a new technique which was based on the DIS system linking MOC system and the HIT and CAT Modules, including the process safety information. The goals of this method are to enhance the safety level and improve the performance efficiency.     

高斯过程及其在软测量建模中的应用

结合工业萘初馏塔关键质量指标估计问题,提出了采用高斯过程（GP）建立复杂工业过程软测量方法。将自动相关确定（ARD）原理与GP模型结合进行软测量模型辅助变量选择,通过建立GP软测量模型,同时得到关键质量指标估计值和相应的预测不确定度,有效解决了现有软测量建模方法不能给出估计值的测量不确定度的问题。研究表明,GP软测量模型不仅能自动选择辅助变量,而且还具有较高的估计精度和较小的测量不确定度,能够更好地满足工业现场对测量可靠性的要求。     

Estimation of void boundaries in flow field using expectation–maximization algorithm

Phase distribution in the flow field provides an insight into the hydrodynamics and heat transfer between the fluids. Void fraction, which is one of the key flow parameters, can be determined by estimating the phase boundaries. Electrical impedance tomography (EIT), which has high temporal characteristics, has been used as an imaging modality to estimate the void boundaries, using the prior knowledge of conductivities. The voids formed within the process vessel are not stable and their movement is random in nature, thus dynamic estimation schemes are necessary to track the fast changes. Kalman-type estimators like extended Kalman filter (EKF) assume the knowledge of model parameters, such as the initial states, state transition matrix and the covariance of process and measurement noise. In real situations, we do not have the prior information of the model parameters; therefore, in such circumstances the estimation performance of the Kalman-type filters is affected. In this paper, the expectation–maximization (EM) algorithm is used as an inverse algorithm to estimate the model parameters as well as non-stationary void boundary. The uncertainties caused in Kalman-type filters, due to the inaccurate selection of model parameters are overcome using an EM algorithm. The performance of the method is tested with numerical and experimental data. The results show that an EM has better estimation of the void boundary as compared to the conventional EKF.     

一种软测量模型性能监测评价及其自适应校正方法

工业过程对象特性变化会导致软测量的测量精度下降甚至失真,需要对软测量模型进行校正。首先构建软测量模型性能评价指标用于对模型性能进行监测。当性能评价指标超过统计限时,对过程特性变化类型进行诊断：若过程特性渐变,则对模型进行递推校正;若过程特性发生突变,则对模型进行重构校正。通过对连续搅拌釜式反应器和DAISY（database for identification of systems）数据库提供的蒸发器实际生产数据进行仿真实验,验证了本文方法的有效性。实验结果表明,该方法避免了传统校正方法存在的盲目校正、受离线分析噪声影响严重等问题,有效地提高了软测量模型对对象特性变化的适应能力。     

Operational optimization of industrial steam systems under uncertainty using data-Driven adaptive robust optimization

This article addresses the operational optimization of industrial steam systems under device efficiency uncertainty using a data-driven adaptive robust optimization approach. A semiempirical model of steam turbine is first developed based on process mechanism and operational data. Uncertain parameters of the proposed steam turbine model are further derived from the historical process data. A robust kernel density estimation method is then used to construct the uncertainty sets for modeling these uncertain parameters. The data-driven uncertainty sets are incorporated into a two-stage adaptive robust mixed-integer linear programming (MILP) framework for operational optimization of steam systems to minimize the total operating cost. Integer variables are introduced to model the on/off decisions of the steam turbines and electrical motors, which are the major energy consumers of the steam system. By applying the affine decision rule, the proposed multilevel optimization model is transformed into its robust counterpart, which is a single-level MILP problem. The proposed framework is applied to the steam system of a real-world ethylene plant to demonstrate its applicability. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16500 2019     

Optimal hybrid modeling approach for polymerization reactors using parameter estimation techniques

The dynamics of polymerization catalytic reactors have been investigated by many researchers during the past five decades; however, the emphasis of these studies was directed towards correlating process model parameters using empirical investigation based on small scale experimental setup and not on real process conditions. The resulting correlations are of limited practical use for industrial scale operations. A statistical study for the relative correlation of each of the effective process parameters revealed the best combination of parameters that could be used for optimizing the process model performance. Parameter estimation techniques are then utilized to find the values of these parameters that minimize a predefined objective function. Published real industrial scale data for the process was used as a basis for validating the process model. To generalize the model, an artificial neural network approach is used to capture the functional relationship of the selected parameters with the process operating conditions. The developed ANN-based correlation was used in a conventional fluidized catalytic bed reactor (FCR) model and simulated under industrial operating conditions. The new hybrid model predictions of the melt-flow index and the emulsion temperature were compared to industrial measurements as well as published models. The predictive quality of the hybrid model was superior to other models. The suggested parameter estimation and modeling approach can be used for process analysis and possible control system design and optimization investigations.     

基于互联网的EPC建材行业智能一体化服务模式

为满足新型智能化生产线的建设需求,以实现全数字化、自动化、知识化和智能化为目标,中国建材国际工程集团有限公司设计研发了基于互联网的工程总承包(EPC)建材行业智能一体化服务模式,将大量工业技术原理、行业知识、基础模型进行规则化、软件化和模块化,封装为可重复使用和灵活调用的微服务,实现工业 APP快速应用部署和网络协同。支持计算、存储和服务等基础管理,同时对生产过程数据进行管控与分析,实现设备状态监测、故障诊断、预测预警、质量控制、工艺控制和经营运维等生产全过程协同管理,形成多项智能工厂系统解决方案,并将相关成果应用于多个示范项目中,加快推进企业转型和建材行业智能化服务战略的落地。     

基于变分贝叶斯算法的线性变参数系统辨识

线性变参数系统（LPV）将多阶段、非线性的过程建模转化为线性多模型的辨识问题,是解决非线性过程建模的一个有效手段。由于实际工业过程存在各种干扰因素,导致被建模系统呈现随机性及模型参数的不确定性。针对这一问题,考虑采用变分贝叶斯（VB）算法对LPV模型进行辨识。该算法首先给定参数相应的先验分布,通过最大化目标函数的下界,从而估计得到参数的后验分布。不仅可实现对参数的点估计,同时量化了估计值的不确定性。针对典型二阶过程和连续搅拌反应釜（CSTR）,运用提出的算法进行仿真实验,表明了该贝叶斯估计方法的优越性。     

An in silico evaluation of data‐driven optimization of biopharmaceutical processes

Two methodological improvements of the design of dynamic experiments (C. Georgakis, Ind Eng Chem Res. 2013) for the modeling and optimization of (semi‐) batch processes are proposed. Their effectiveness is evaluated in two representative classes of biopharmaceutical processes. First, we incorporate prior process knowledge in the design of the experiments. Many batch processes and, in particular, biopharmaceutical processes are usually not understood completely to enable the development of an accurate knowledge‐driven model. However, partial process knowledge is often available and should not be ignored. We demonstrate here how to incorporate such knowledge. Second, we introduce an evolutionary modeling and optimization approach to minimize the initial number of experiments in the face of budgetary and time constraints. The proposed approach starts with the estimation of only a linear Response Surface Model, which requires the minimum number of experiments. Accounting for the model's uncertainty, the proposed approach calculates a process optimum that meets a maximum uncertainty constraint. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2796–2805, 2017     

Use of tendency models and their uncertainty in the design of state estimators for batch reactors

Tendency models have been successful in the modeling and optimization of batch reactor processes where a detailed understanding based on fundamental principles and detailed kinetic studies is not available. The evolutionary nature of the Tendency modeling algorithm has proven useful in updating the process model between batches, as new process data or insight become available. But optimization is not the only task that can be undertaken with a Tendency model. In this work, the use of Tendency models in the design of state estimators to estimate reactor concentrations is investigated. The primary goal is to use the knowledge of the uncertainty in the Tendency model (which, by its nature, is an approximate model) to tune an extended Kalman filter. Two examples are presented to illustrate that even though Tendency models can feature a significant amount of uncertainty, they can be used successfully in state estimators.     

Identification of kinetic models of methanol oxidation on silver in the presence of uncertain catalyst behavior

Catalytic oxidation of methanol to formaldehyde is an important industrial process due to the value of formaldehyde either as a final product or as a precursor of numerous chemicals. The study of kinetics in this system is hindered by sources of uncertainty that are inherently associated to the nature and state of the catalyst (e.g., uncertain reactivity level, deactivation phenomena), the measurement system and the structure of the kinetic model equations. In this work, a simplified kinetic model is identified from data collected from continuous flow microreactor systems where catalysts with assorted levels of reactivity are employed. Tailored model-based data mining methods are proposed and applied for the effective estimation of the kinetic parameters and for identifying robust experimental conditions to be exploited for the kinetic characterization of catalysts with different reactivity, whose kinetic behavior is yet to be investigated.     

From process simulation to general estimation

The ability to use a simulation program with the aim of making mass and energy balances in a plant, starting from just raw data and process knowledge, is an ambitious and attractive goal. We propose to address a general formulation for the estimation problem in the framework of a simultaneous modular approach. As raw data are corrected to satisfy mass and energy balances and some parameters are computed to give a unit operation performance diagnosis, the estimation consists both of data reconciliation and parameter identification. It follows that the numerical problem is a minimization of a least square objective function. Constrained minimization is performed with an infeasible path strategy which solves the optimization problem and process balance convergence at the same level, with a nonlinear programming code. The confidence intervals computation, for parameters and corrected values of measurements, is also discussed. A simple flash loop flowsheet example gives a better understanding of the theoretical aspects. A more complex flowsheet has been chosen as a second illustrative example.     

Model-based fault diagnosis for hybrid systems: Application on chemical processes

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless, this information is imprecise because of process and measurement noise. So, the research ways aim at developing new and more powerful techniques for the detection of process fault. This article presents a method for the fault detection based on the comparison between the reference model evolution and the real system generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering.     

The use of expert systems in chemical engineering

Expert system first came into use in the fields of medicine (MYCIN) and geology (PROSPECTOR). They have recently established—one could almost say very quickly—their position in the field of chemical engineering. Based on the experience gained until now, we realize that this is only the beginning of a vast development.The topic of this work is the expert system PROSPERT, which was developed at the ‘Lehrstuhl für Technische Chemie A’ of the University of Dortmund. With a few examples, the basic ability to generate process design proposals with PROSPERT that are very similar to the processes in practical industrial use has been demonstrated.This expert system can be used as the basis for the development of a ‘front program’ that can be consulted before extensive optimization calculations are executed using modern simulation programs. With the help of PROSPERT, the multitude of possible design alternatives can be reduced to a reasonable number. The required input data for a problem can usually be obtained easily, and in most of the cases only qualitative statements are needed. The system can run on a higher quality personal computer (e.g. an IBM-AT, 640 kB, 20 MB on hard disk). The system is written in LPA-PROLOG-Professional in combination with the toolkit APES (Augmented PROLOG for Expert Systems).The work we have done until now should verify the basic practicality of the use of expert systems in the range of process design, using only limited domain knowledge. This procedure is referred to as ‘rapid prototyping’, because the declarative programming languages like PROLOG allow fast conversion of verbal formulations to computer programs and an easy test of a new rule using the explanatory component.The further development of PROSPERT is directed towards an extension of the knowledge base, where additional use of the so-called ‘vague knowledge’ shall be made applicable to the system.With a few examples, the basic ability to generate process design proposals with PROSPERT that are very similar to the processes in practical industrial use has been demonstrated.Thus, this expert system can be used as the basis for the development of a ‘front program’ that can be consulted before extensive optimization calculations are executed using modern simulation programs. With the help of PROSPERT, the multitude of possible design alternatives can be reduced to a reassonable number. The required input data for a problem can usually be obtained easily, and in most of the cases only qualitative statements are needed.The system can run on a higher quality personal computer (e.g. an IBM-AT, 640 kB, 20 MB on hard disk). The system is written in LPA-PROLOG-Professional in combination with the toolkit APES (Augmented PROLOG for Expert Systems).The further development of PROSPERT is directed towards an extension of the knowledge base, where additional use of the so-called ‘vague knowledge’ shall be made applicable for the system. For example: a heuristic rule prohibits the use of a distillation if the boiling points of the components are ‘very near’. Up to now this vague statement is implemented with the use of a fixed limit value. In the future, the definition ‘very near’ will be represented using the so-called ‘fuzzy sets’. Every vague value will then be related to a specific probability so that PROSPERT will be able to provide the user with a certainty factor for each of its proposed designs.     

基于FGCN的针铁矿沉铁过程建模

针铁矿沉铁过程是由多个连续反应器级联,并且包含氧化反应、还原反应以及中和反应等一系列复杂化学反应的复杂过程,具有强非线性、不确定性的特点,难以建立精确的数学模型。提出一种基于模糊灰色认知网络（fuzzy gray cognitive network,FGCN）的针铁矿沉铁过程的建模方法。根据专家经验和历史数据,建立针铁矿沉铁系统的模糊灰色认知网络模型,利用带终端约束的非线性Hebbian学习算法（nonlinear Hebbian learning,NHL）对权值进行学习。在不同程度的不确定性环境下对系统进行分析,结果表明模糊灰色认知网络能够在不确定性高的环境下对复杂工业系统进行有效模拟,收敛到一个灰度为零或者灰度很小的灰数平衡点,利用白化函数得到一个准确的控制输出。     

聚合反应过程质量指标的推理估计混合模型

针对聚合反应过程的非线性、时变性和不确定性，提出了一种多类型混联混合推理估计模型。该模型以过程机理知识为基础框架，以各种神经网络和回归辩识模型的计算结果作为混合模型中各子模型或机理模型的过程参数。为了体现过程的多模式集成特点，该混合模型充分利用各种类型模型的不同特性，既保证按照动力学规律描述聚合反应过程特性，又充分利用现场运行和分析的数据，辩识模型结构参数，使所建模型不必完全依赖对过程特性的认识。将该混合模型用于聚丙烯腈生产过程质量指标的推理估计，现场应用效果证明了这种模型的优良性能。     

EXTENDED KALMAN FILTER CONTROLLER: FIRST PRINCIPLES MODELS TO NEURAL NETWORKS

Extended Kalman filters (EKF) have been widely employed for state and parameter estimation in chemical engineering systems. Gao et al. [Gao, F., Wang, F. and Li, M. (1999). Ind. Eng. Chem. Res., 38, 2345-2349] have proposed the use of EKF for control computation using a neural network representation of the system in a discrete-time framework. In the present study, an EKF controller is proposed in a continuous time framework with models incorporating different levels of process knowledge. The problem of process-model mismatch is handled by incorporating EKF-based state and/or parameter estimation along with control computation. A batch reactor temperature control problem for a highly exothermic reaction between maleic anhydride and hexanol to form hexyl monoester of maleic acid is considered as a test bed to evaluate the performance of the proposed control schemes. Three different models are considered, namely the first principles model, a reduced-order process model, and an artificial neural network (ANN) model for formulation of the control schemes. The performance of the proposed control scheme using first principles model is compared to that of generic model control, and a similar performance is achieved. The present study illustrates the usefulness of the proposed control schemes and can be easily extended to general chemical engineering systems.