Heat exchanger network synthesis with detailed exchanger designs—2. Hybrid optimization strategy for synthesis of heat exchanger networks

We propose a new strategy to synthesize heat exchanger networks with detailed designs of individual heat exchangers. The proposed strategy uses a multistep approach by first obtaining a heat exchanger network topology through solving a modified version of the mixed integer nonlinear programming (MINLP) stage-wise superstructure of Yee and Grossmann, which includes a smoothed LMTD approximation and pressure drops. In a second nonlinear programming (NLP) suboptimization step, we allow for nonisothermal mixing to solve problems with or without exchanger bypasses. The selected heat exchangers along with the mass and energy balances obtained are then used to design the network with detailed exchanger designs through solving a sequence of NLPs for individual heat exchanger designs. The NLPs are based on the detailed discretized optimization models of Kazi et al., which solve quickly and reliably to obtain heat exchangers based on rigorous, first-principles derived coupled differential equations. These models solve a differential algebraic equation system and do not rely on usual assumptions associated with other heuristic-based exchanger design methods, such as log mean temperature difference and F_T correction factors. These detailed exchanger designs are then used to update the network optimization model through sets of correction factors on heat exchanger area, number of shells, heat transfer coefficients, and pressure drops of each exchanger design, in a method based on that of Short et al. The method solves reliably, guaranteeing feasible exchangers for every potential network generated by the shortcut models, through validation with rigorous heat exchanger models at every iteration. In addition, the method does not increase the nonlinearity of the MINLP model, nor does it require any manual intervention or initialization from the user. Three examples are solved and the results are compared to those obtained in the literature.     

Application of support vector regression for developing soft sensors for nonlinear processes

The field of soft sensor development has gained significant importance in the recent past with the development of efficient and easily employable computational tools for this purpose. The basic idea is to convert the information contained in the input–output data collected from the process into a mathematical model. Such a mathematical model can be used as a cost efficient substitute for hardware sensors. The Support Vector Regression (SVR) tool is one such computational tool that has recently received much attention in the system identification literature, especially because of its successes in building nonlinear blackbox models. The main feature of the algorithm is the use of a nonlinear kernel transformation to map the input variables into a feature space so that their relationship with the output variable becomes linear in the transformed space. This method has excellent generalisation capabilities to high‐dimensional nonlinear problems due to the use of functions such as the radial basis functions which have good approximation capabilities as kernels. Another attractive feature of the method is its convex optimization formulation which eradicates the problem of local minima while identifying the nonlinear models. In this work, we demonstrate the application of SVR as an efficient and easy‐to‐use tool for developing soft sensors for nonlinear processes. In an industrial case study, we illustrate the development of a steady‐state Melt Index soft sensor for an industrial scale ethylene vinyl acetate (EVA) polymer extrusion process using SVR. The SVR‐based soft sensor, valid over a wide range of melt indices, outperformed the existing nonlinear least‐square‐based soft sensor in terms of lower prediction errors. In the remaining two other case studies, we demonstrate the application of SVR for developing soft sensors in the form of dynamic models for two nonlinear processes: a simulated pH neutralisation process and a laboratory scale twin screw polymer extrusion process. A heuristic procedure is proposed for developing a dynamic nonlinear‐ARX model‐based soft sensor using SVR, in which the optimal delay and orders are automatically arrived at using the input–output data.     

粒子滤波算法测定混合染液染料浓度

针对如何准确测定混合染液染料浓度,尤其当混合染液浓度太大时由于染料自身发生如水解等化学变化以及染料之间的相互作用,其吸光度体系呈非线性的情况,提出了通过建立非线性吸光度模型,并利用粒子滤波算法同时测定混合染液中各染料浓度的解决方法。以3种活性染料的混合染液为例,对该方法的可行性进行验证,并与扩展卡尔曼滤波算法进行滤波效果对比。实例证明,非线性吸光度模型是准确的,基于粒子滤波算法的测定方法是有效可行的。     

CONTROL AND ESTIMATION FOR PERTURBED NONLINEAR PROCESSES WITH DEADTIME

Feedback linearization techniques are used to deal with the nonlinear controller designs which have attracted many researchers' attention in recent years. The approach has been applied successfully to solve a number of practical nonlinear control problems, but typically requires on-line full state measurement which is usually not the case in real chemical process industries. In this paper, we address the problem of synthesizing nonlinear state feedback controllers for time-delay nonlinear systems which are perturbed by disturbances. On-line estimation of the unmeasurable disturbances and unavailable state variables is introduced to facilitate the implementation of coordinate transformations and state feedback and prediction. Two kinds of dynamic compensators are then proposed to handle the process deadtime. Finally numerical simulations in a CSTR example demonstrate the promising performance of the overall nonlinear control structure in disturbance rejection.     

Wastewater reuse: a new approach to screen for designs with minimal total costs

Current process integration tools dealing with the generation of optimal wastewater reuse designs require a lot of effort for data acquisition and frequently suggest designs which are rejected by the process owners due to the ignored constraints or expensive piping needs. In this paper we present a mixed integer linear programming (MILP) model that overcomes both criticisms. Only easily accessible data such as process location, current water demand, and binary information on the reuse possibilities of wastewater streams are required. The total costs, comprising operating costs (freshwater, wastewater treatment, pumping) and investment costs (piping, holding tanks), are minimised for a given time horizon. One characteristic of calculating the piping cost is that splitting and merging of pipes are taken into account. The model was applied to an industrial case study for which several reuse designs were generated and discussed with regard to economical (payback time, investment costs), ecological (total flow rate), and technical aspects.     

基于asNMPC方法的非线性网络化实时反馈控制

在网络化控制系统中,网络的介入带来了一些不利于系统控制的问题,如数据传输中普遍存在的随机时变延时,数据丢失和数据时序颠倒等。在预测控制思想的基础上,针对前馈通道和反馈通道同时存在网络传输环节的情况,采用asNMPC(advanced-step nonlinear model predictive control)方法实现了对于非线性网络化控制系统中上述问题的补偿控制。模型失配情况下与DMC(dynamic matrix control)进行比较,仿真结果表明了asNMPC方法对于非线性网络化控制系统的有效性。     

Improving scenario decomposition algorithms for robust nonlinear model predictive control

This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.     

A generalized cutting-set approach for nonlinear robust optimization in process systems engineering

We propose a novel computational framework for the robust optimization of highly nonlinear, non-convex models that possess uncertainty in their parameter data. The proposed method is a generalization of the robust cutting-set algorithm that can handle models containing irremovable equality constraints, as is often the case with models in the process systems engineering domain. Additionally, we accommodate general forms of decision rules to facilitate recourse in second-stage (control) variables. In particular, we compare and contrast the use of various types of decision rules, including quadratic ones, which we show in certain examples to be able to decrease the overall price of robustness. Our proposed approach is demonstrated on three process flow sheet models, including a relatively complex model for amine-based CO₂ capture. We thus verify that the generalization of the robust cutting-set algorithm allows for the facile identification of robust feasible designs for process systems of practical relevance.     

Design and assessment of delay timer alarm systems for nonlinear chemical processes

In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this article, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time‐series models. The alarm design for such complex processes faces three important challenges: (1) industrial processes exhibit highly nonlinear behavior; (2) state variables are not precisely known (modeling error); and (3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this article, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates—two common performance measures for alarm systems. To ensure the alarm design is robust to any non‐stationary process behavior, an expected‐case and a worst‐case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non‐stationary chemical reactor problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 77–90, 2018     

APPLICATION OF THE TRANSFORM-ITERATIVE METHOD TO NONLINEAR CONCENTRATION BOUNDARY VALUE PROBLEMS

In this paper we present a “somewhat” new method that treats nonlinear concentration related boundary value problems, among other problems. The method uses a general convolution product to transform the nonlinear term, then it iterates with successive division inside the integral to arrive at the approximate solution. It will also include an explanation to the apparent success of what was recently termed “novel transform method” for such nonlinear problems, as being a very special case. As such, it represents the first mathematical basis of any kind, that would offer a reason for the limited success of the recent “novel” method. The present method is illustrated here, for a porous catalyst slab with an outline for other geometries and different non-linearities. The numerical results agree very well with the known exact results of these problems, and other problems in different fields.     

Synthesis and optimization of membrane cascade for gas separation via mixed‐integer nonlinear programming

Currently, membrane gas separation systems enjoy widespread acceptance in industry as multistage systems are needed to achieve high recovery and high product purity simultaneously, many such configurations are possible. These designs rely on the process engineer's experience and therefore suboptimal configurations are often the result. This article proposes a systematic methodology for obtaining the optimal multistage membrane flow sheet and corresponding operating conditions. The new approach is applied to cross‐flow membrane modules that separate CO₂ from CH₄, for which the optimization of the proposed superstructure has been achieved via a mixed‐integer nonlinear programming model, with the gas processing cost as objective function. The novelty of this work resides in the large number of possible interconnections between each membrane module, the energy recovery from the high pressure outlet stream and allowing for nonisothermal conditions. The results presented in this work comprise the optimal flow sheet and operating conditions of two case studies. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1989–2006, 2017     

Simultaneous synthesis of structural‐constrained heat exchanger networks with and without stream splits

This paper presents a comprehensive simultaneous synthesis approach based on stage‐wise superstructure to design cost‐optimal heat exchanger network (HEN). It is well known that the simultaneous synthesis model has very complicated mixed integer nonlinear programming formulations, which are non‐convex, non‐continuous and have many local optima. Up till now, it cannot be expected that an algorithm can find, in polynomial time, the global solution to the simultaneous synthesis problem of HEN. In order to reduce computational complexity, some simplified assumptions for structures, such as no stream splits, stream splits with isothermal mixing, no stream split flowing through more than one exchanger, etc, are adopted to prune the search space at the expense of neglecting certain important alternatives in the network configuration. In this work, a flexible stage‐wise superstructure is proposed to control the solution performance and search space efficiently. At each stage of the superstructure, with or without stream splits is determined at random or by the experience of designers. In this way, various candidate series and split network designs featuring the lowest annual cost can be found. Moreover, an efficient two‐level optimisation algorithm is employed for solving the presented model utilising genetic algorithm and particle swarm optimisation algorithm. Three case studies are presented to show the applicability of the proposed methodology. In addition, the results show that the new approach is able to find more economical networks than those generated by other methods. © 2012 Canadian Society for Chemical Engineering     

Optimal design of reverse osmosis‐based water treatment systems

We address the problem of optimal design of reverse osmosis (RO)‐based water treatment systems. A superstructure optimization method is proposed to solve the problem, where the superstructure for a RO system is structurally enhanced with additional features. We formulate the problem as mixed‐integer nonlinear program which is solved to yield optimal results. A case study on desalination is considered in this work, and the numerical results obtained using our approach are validated using a commercial simulation tool. We further extend the problem by considering the effects of degradation of membrane performance over time and solve it by representing the problem as a two‐stage stochastic program. This new approach is highly useful for identifying minimum cost robust designs for membrane‐based water purification systems, which are especially important in desalination applications. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Modeling and simulation of stretch blow molding of polyethylene terephthalate

When polyethylene terephthalate (PET) is stretched, it exhibits strain‐hardening properties, which are temperature and strain‐rate dependent. In this paper, two grades of PET are experimentally characterized using biaxial tests. A visco‐hyperelastic model is used to describe the stretching behavior for the polymer. A biaxial characterization method is employed to determine the model parameters using a robust nonlinear curve‐fitting program. This model can represent adequately well the stretching behavior of PET. Based on this model, the membrane finite element formulation is developed to simulate the stretch blow molding process. Two bottles of different designs, produced based on the single‐stage injection blow molding process, are used to validate the model. Good agreement with the bottle thickness profile is observed. Polym. Eng. Sci. 44:1460–1472, 2004. © 2004 Society of Plastics Engineers.     

Nonlinear stochastic modeling to improve state estimation in process monitoring and control

State estimation from plant measurements plays an important role in advanced monitoring and control technologies, especially for chemical processes with nonlinear dynamics and significant levels of process and sensor noise. Several types of state estimators have been shown to provide high‐quality estimates that are robust to significant process disturbances and model errors. These estimators require a dynamic model of the process, including the statistics of the stochastic disturbances affecting the states and measurements. The goal of this article is to introduce a design method for nonlinear state estimation including the following steps: (i) nonlinear process model selection, (ii) stochastic disturbance model selection, (iii) covariance identification from operating data, and (iv) estimator selection and implementation. Results on the implementation of this design method in nonlinear examples (CSTR and large dimensional polymerization process) show that the linear time‐varying autocovariance least‐squares technique accurately estimates the noise covariances for the examples analyzed, providing a good set of such covariances for the state estimators implemented. On the estimation implementation, a case study of a chemical reactor demonstrates the better capabilities of MHE when compared with the extended Kalman filter. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

非线性规划在水泥工业工程设计中的应用

本文主要利用现有非线性规划方法，结合具体的运用研究和工程开发设计的需要，建立了一系列非线性规划问题．并针对问题的性质，进行分析求解，解决了工程中的一系列实际问题，同时也展示非线性规划在水泥工程应用的作用．现已成为水泥工业技术装备及过程开发、研究和设计的有力控制工具，为提高行业的工程研究、开发和装备的技术水平及控制和优化提供了有效途径。     

Optimal synthesis of p‐xylene separation processes based on crystallization technology

This article addresses the synthesis and optimization of crystallization processes for p‐xylene recovery for systems with feed streams of high concentration, a case that arises in hybrid designs where the first step is commonly performed by adsorption. A novel superstructure and its corresponding mixed‐integer nonlinear programming (MINLP) model are proposed. The distinct feature of this superstructure is the capability to generate optimum or near optimum flow sheets for a wide range of specifications of p‐xylene compositions in the feed stream of the process. To cope with the complexity of the MINLP model, a two‐level decomposition approach, consisting of the solution of an aggregated model and a detailed model, is proposed. The results obtained show good performance of the decomposition strategy, and the optimal flow sheets and p‐xylene recoveries are in agreement with the results reported in patents. © 2008 American Institute of Chemical Engineers AIChE J, 2009     

Economic model predictive control of nonlinear process systems using Lyapunov techniques

In this work, we develop model predictive control (MPC) designs, which are capable of optimizing closed‐loop performance with respect to general economic considerations for a broad class of nonlinear process systems. Specifically, in the proposed designs, the economic MPC optimizes a cost function, which is related directly to desired economic considerations and is not necessarily dependent on a steady‐state—unlike conventional MPC designs. First, we consider nonlinear systems with synchronous measurement sampling and uncertain variables. The proposed economic MPC is designed via Lyapunov‐based techniques and has two different operation modes. The first operation mode corresponds to the period in which the cost function should be optimized (e.g., normal production period); and in this operation mode, the MPC maintains the closed‐loop system state within a predefined stability region and optimizes the cost function to its maximum extent. The second operation mode corresponds to operation in which the system is driven by the economic MPC to an appropriate steady‐state. In this operation mode, suitable Lyapunov‐based constraints are incorporated in the economic MPC design to guarantee that the closed‐loop system state is always bounded in the predefined stability region and is ultimately bounded in a small region containing the origin. Subsequently, we extend the results to nonlinear systems subject to asynchronous and delayed measurements and uncertain variables. Under the assumptions that there exist an upper bound on the interval between two consecutive asynchronous measurements and an upper bound on the maximum measurement delay, an economic MPC design which takes explicitly into account asynchronous and delayed measurements and enforces closed‐loop stability is proposed. All the proposed economic MPC designs are illustrated through a chemical process example and their performance and robustness are evaluated through simulations. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Weighted support vector machine for quality estimation in polymerization processes

In this paper, a modified version of the Support Vector Machine (SVM) is proposed as an empirical model for polymerization processes modeling. Usually the exact principle models of polymerization processes are seldom known; therefore, the relations between input and output variables have to be estimated by using an empirical inference model. They can be used in process monitoring, optimization and quality control. The Support Vector Machine is a good tool for modeling polymerization process because it can handle highly nonlinear systems successfully. The proposed method is derived by modifying the risk function of the standard Support Vector Machine by using the concept of Locally Weighted Regression. Based on the smoothness concept, it can handle the correlations among many process variables and nonlinearities more effectively. Case studies show that the proposed method exhibits superior performance as compared with the standard SVR, which is itself superior to the traditional statistical learning machine in the case of high dimensional, sparse and nonlinear data.     

Multi-loop nonlinear internal model controller design based on a dynamic fuzzy partial least squares model

In this paper, a dynamic fuzzy partial least squares (DFPLS) modeling method is proposed. Under such framework, the multiple input multiple output (MIMO) nonlinear system can be automatically decomposed into several univariate subsystems in PLS latent space. Within each latent space, a dynamic fuzzy method is introduced to model the inherent dynamic and nonlinear feature of the physical system. The new modeling method combines the decoupling characteristic of PLS framework and the ability of dynamic nonlinear modeling in the fuzzy method. Based on the DFPLS model, a multi-loop nonlinear internal model control (IMC) strategy is proposed. A pH neutralization process and a methylcyclohexane (MCH) distillation column from Aspen Dynamic Module are presented to demonstrate the effectiveness of the proposed modeling method and control strategy.