State‐of‐the‐art and progress in the optimization‐based simultaneous design and control for chemical processes

Significant progress in the area of simultaneous design and control for chemical processes has been achieved and various methodologies have been put forward to address this issue over the last several decades. These methods can be classified in two categories (1) controllability indicator‐based frameworks that are capable of screening alternative designs, and (2) optimization‐based frameworks that integrate the process design and control system design. The major objective is to give an up‐to‐date review of the state‐of‐the‐art and progress in the challenging area of optimization‐based simultaneous design and control. First, motivations and significances of simultaneous design and control are illustrated. Second, a general classification of existing methodologies of optimization‐based simultaneous design and control is outlined. Subsequently, the mathematical formulations and relevant theoretical solution algorithms, their merits, strengths and shortcomings are highlighted. Last, based on the recent advances in this field, challenges and future research directions are discussed briefly. An attempt is made with the help of this review article to stimulate further research and disseminate the simultaneous design methods to challenging problem areas. In particular, the application of optimization‐based simultaneous design and control methods to large‐scale systems with highly inherent nonlinear dynamics often the case in industrial chemical processes remains a challenging task and yet to be solved. © 2012 American Institute of Chemical Engineers AIChE J, 58: 1640–1659, 2012     

Multiway discrete hidden Markov model‐based approach for dynamic batch process monitoring and fault classification

A new multiway discrete hidden Markov model (MDHMM)‐based approach is proposed in this article for fault detection and classification in complex batch or semibatch process with inherent dynamics and system uncertainty. The probabilistic inference along the state transitions in MDHMM can effectively extract the dynamic and stochastic patterns in the process operation. Furthermore, the used multiway analysis is able to transform the three‐dimensional (3‐D) data matrices into 2‐D measurement‐state data sets for hidden Markov model estimation and state path optimization. The proposed MDHMM approach is applied to fed‐batch penicillin fermentation process and compared to the conventional multiway principal component analysis (MPCA) and multiway dynamic principal component analysis (MDPCA) methods in three faulty scenarios. The monitoring results demonstrate that the MDHMM approach is superior to both the MPCA and MDPCA methods in terms of fault detection and false alarm rates. In addition, the supervised MDHMM approach is able to classify different types of process faults with high fidelity. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

动态过程优化算法的计算量比较

A comparison of arithmetic operations of two dynamic process optimization approaches called quasi-sequential approach and reduced Sequential Quadratic Programming (rSQP) simultaneous approach with respect to equality constrained optimization problems is presented. Through the detail comparison of arithmetic operations, it is concluded that the average iteration number within differential algebraic equations (DAEs) integration of quasi-sequential approach could be regarded as a criterion. One formula is given to calculate the threshold value of average iteration number. If the average iteration number is less than the threshold value, quasi-sequential approach takes advantage of rSQP simultaneous approach which is more suitable contrarily. Two optimal control problems are given to demonstrate the usage of threshold value. For optimal control problems whose objective is to stay near desired operating point, the iteration number is usually small. Therefore, quasi-sequential approach seems more suitable for such problems.     

Simultaneous process synthesis and control design under uncertainty: A worst‐case performance approach

A new methodology that includes process synthesis and control structure decisions for the optimal process and control design of dynamic systems under uncertainty is presented. The method integrates dynamic flexibility and dynamic feasibility in a single optimization formulation, thus, reducing the costs to assess the optimal design. A robust stability test is also included in the proposed method to ensure that the optimal design is stable in the presence of magnitude‐bounded perturbations. Since disturbances are treated as stochastic time‐discrete unmeasured inputs, the optimal process synthesis and control design specified by this method remains feasible and stable in the presence of the most critical realizations in the disturbances. The proposed methodology has been applied to simultaneously design and control a system of CSTRs and a ternary distillation column. A study on the computational costs associated with this method is presented and compared to that required by a dynamic optimization‐based scheme. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2497–2514, 2013     

Multi‐objective optimization superimposed model‐based process design of an enzymatic hydrolysis process

The concepts of green process engineering and rigorous model‐based approaches have proven to be highly beneficial in process engineering. Although a combination of these two principles thus appears extremely promising, it is not found very commonly in literature. The very high complexity resulting from this combination poses great challenges for the process design and design engineers. Therefore, this work presents an innovative methodology for the model‐based process design with superimposed multi‐objective optimization for an exemplary process. This process for the enzymatic hydrolysis of fatty acid methyl ester combines several aspects of green process engineering and represents an exemplary process with an enzymatic liquid‐liquid‐solid reaction system. The optimization results based on operating and investment costs reveal important insights on the exemplary process and highlight the great advantages of the developed methodology as a profound basis for academic and industrial process design purposes. © 2017 American Institute of Chemical Engineers AIChE J, 63: 1974–1988, 2017     

Error‐triggered on‐line model identification for model‐based feedback control

In industry, it may be difficult in many applications to obtain a first‐principles model of the process, in which case a linear empirical model constructed using process data may be used in the design of a feedback controller. However, linear empirical models may not capture the nonlinear dynamics over a wide region of state‐space and may also perform poorly when significant plant variations and disturbances occur. In the present work, an error‐triggered on‐line model identification approach is introduced for closed‐loop systems under model‐based feedback control strategies. The linear models are re‐identified on‐line when significant prediction errors occur. A moving horizon error detector is used to quantify the model accuracy and to trigger the model re‐identification on‐line when necessary. The proposed approach is demonstrated through two chemical process examples using a model‐based feedback control strategy termed Lyapunov‐based economic model predictive control (LEMPC). The chemical process examples illustrate that the proposed error‐triggered on‐line model identification strategy can be used to obtain more accurate state predictions to improve process economics while maintaining closed‐loop stability of the process under LEMPC. © 2016 American Institute of Chemical Engineers AIChE J, 63: 949–966, 2017     

A calibration methodology and model‐based systems analysis for SBRs removing nutrients under limited aeration conditions

A methodology is proposed for the model calibration of nutrient‐removing laboratory‐scale SBRs under limited aeration. Based on in‐process measurements and influent wastewater characterization, the ASM2d model was modified by adding an organic nitrogen module incorporating a hydrolysis mechanism. After calibration, the simulation results showed that enhanced biological nutrient removal occurred during the fill period and under reduced aeration achieving so‐called ‘simultaneous nutrient removal’. A model‐based systems analysis was performed in terms of the contributions of different processes to overall oxygen, nitrogen and phosphate utilization. In each phase, simultaneously occurring biological reactions were compared using the calibrated model. According to the calibrated model, 61% of all denitrified nitrate is denitrified during the mixing/filling phase. On the other hand, 17% of all denitrified nitrate is consumed by simultaneous nitrification–denitrification during the first aerobic period. The aerobic and anoxic P‐removals were quantified as 73% and 12%, respectively. Copyright © 2006 Society of Chemical Industry     

A backoff strategy for model‐based experiment design under parametric uncertainty

Model‐based experiment design techniques are an effective tool for the rapid development and assessment of dynamic deterministic models, yielding the most informative process data to be used for the estimation of the process model parameters. A particular advantage of the model‐based approach is that it permits the definition of a set of constraints on the experiment design variables and on the predicted responses. However, uncertainty in the model parameters can lead the constrained design procedure to predict experiments that turn out to be, in practice, suboptimal, thus decreasing the effectiveness of the experiment design session. Additionally, in the presence of parametric mismatch, the feasibility constraints may well turn out to be violated when that optimally designed experiment is performed, leading in the best case to less informative data sets or, in the worst case, to an infeasible or unsafe experiment. In this article, a general methodology is proposed to formulate and solve the experiment design problem by explicitly taking into account the presence of parametric uncertainty, so as to ensure both feasibility and optimality of the planned experiment. A prediction of the system responses for the given parameter distribution is used to evaluate and update suitable backoffs from the nominal constraints, which are used in the design session to keep the system within a feasible region with specified probability. This approach is particularly useful when designing optimal experiments starting from limited preliminary knowledge of the parameter set, with great improvement in terms of design efficiency and flexibility of the overall iterative model development scheme. The effectiveness of the proposed methodology is demonstrated and discussed by simulation through two illustrative case studies concerning the parameter identification of physiological models related to diabetes and cancer care. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

A model‐based approach to the automatic diagnosis of von Willebrand disease

Von Willebrand disease (VWD) is the most common inherited coagulation disorder to be seen in humans. It originates from a deficiency and/or dysfunction of the von Willebrand factor (VWF), a large multimeric glycoprotein playing a central role in the hemostasis process. VWD occurs in a large variety of forms, and its symptoms may range from sporadic nosebleeds and mild bleeding from small lesions in skin, to acute thrombocytopenia or prolonged bleeding episodes. Diagnosing VWD may be complicated because of the heterogeneous nature of the disorder. Two mechanistic models of VWD are proposed in this article, and their performance is assessed using clinical data. Models allow for the automatic detection of the disease, as well as for a quantitative assessment of VWF multimer distribution patterns, thus elucidating the critical pathways involved in the disease recognition and characterization. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1718–1727, 2014     

Improving the design of depth filters: A model‐based method using optimal control theory

Because there is no general design method for depth filters, especially for layered configurations, this methodological gap is addressed here. Using optimal control theory, paths of the filter coefficient, a measure for local filtration performance, are determined along the filter depth. An analytical optimal control solution is derived and used to validate the numerical algorithm. Two optimal control scenarios are solved numerically: In the first scenario, the goal of constant deposition along the filter depth is addressed. The second scenario aims at maximizing the time until some maximal pressure drop is reached. Furthermore, a computational strategy is presented to derive discrete layers suitable for practical design from the continuous optimal control solutions. All optimized scenarios are compared to one‐layered filter designs and significant improvements are found. As this work is based on strongly validated and widely used filtration models, the presented methods are expected to have broad applicability. © 2017 American Institute of Chemical Engineers AIChE J, 63: 68–76, 2018     

Simultaneous measurement of resistance and viscoelastic responses of carbon black‐filled high‐density polyethylene subjected to dynamic torsion

The conduction and viscoelastic responses to temperature are measured simultaneously for carbon black (CB) filled high‐density polyethylene (HDPE) subjected to dynamic torsion. PTC/NTC transition was correlated with the loss tangent peak and the quasi modulus plateau, which was ascribed to the filler network. The bond‐bending model of elastic percolation networks was used to reveal the structural mechanisms for the cyclic resistance changes at different temperatures. The resistance changes at lower temperatures depended on the deformation of the polymer matrix, while the changes in melting state were mainly attributed to the rearrangement of the CB network. A simple scaling law is derived to relate resistance and dynamic storage modulus in the melting region. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

A model‐based virtual sensing approach for the injection molding process

This paper proposes a new virtual sensing approach for on‐line monitoring and regulating process variables of the injection molding process. Based on the nonlinear observer theory, virtual sensors estimate process behavior using easily obtained measurements of machine variables by exploiting their dynamic interaction. A nozzle pressure virtual sensor during the so‐called “nozzle resistance test” was developed. Results of experimental evaluation on a commercial injection molding machine establish the feasibility of the proposed virtual sensing approach. Furthermore, a sensitivity analysis indicates that the proposed virtual sensor delivered consistently better and more robust performance against parametric uncertainties than simple open‐loop model prediction. Polym. Eng. Sci. 44:1605–1614, 2004. © 2004 Society of Plastics Engineers.     

Optimal design of large‐scale chemical processes under uncertainty: A ranking‐based approach

An approach for the optimal design of chemical processes in the presence of uncertainty was presented. The key idea in this work is to approximate the process constraint functions and model outputs using Power Series Expansions (PSE)‐based functions. The PSE functions are used to efficiently identify the variability in the process constraint functions and model outputs due to multiple realizations in the uncertain parameters using Monte Carlo (MC) sampling methods. A ranking‐based approach is adopted here where the user can assign priorities or probabilities of satisfaction for the different process constraints and model outputs considered in the analysis. The methodology was tested on a reactor–heat exchanger system and the Tennessee Eastman process. The results show that the present method is computationally attractive since the optimal process design is accomplished in shorter computational times when compared to the use of the MC method applied to the full plant model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3243–3257, 2014     

Robust batch‐to‐batch optimization in the presence of model‐plant mismatch and input uncertainty

In model‐based optimization in the presence of model‐plant mismatch, the set of model parameter estimates which satisfy an identification objective may not result in an accurate prediction of the gradients of the cost‐function and constraints. To ensure convergence to the optimum, the predicted gradients can be forced to match the measured gradients by adapting the model parameters. Since updating all available parameters is impractical due to estimability problems and overfitting, there is a motivation for adapting a subset of parameters for updating the predicted outputs and gradients. This article presents an approach to select a subset of parameters based on the sensitivities of the model outputs and of the cost function and constraint gradients. Furthermore, robustness to uncertainties in initial batch conditions is introduced using a robust formulation based on polynomial chaos expansions. The improvements in convergence to the process optimum and robustness are illustrated using a fed‐batch bioprocess. © 2017 American Institute of Chemical Engineers AIChE J, 63: 2660–2670, 2017     

An optimization‐based approach for structural design of self‐assembled DNA tiles

DNA tiles are self‐assembled nanostructures, which offer exciting opportunities for synthesis of novel materials. A challenge for structural design of DNA tiles is to identify optimal locations for so‐called crossovers, which are bridges between DNA double helices formed by pairs of single‐stranded DNA. An optimization‐based approach is presented to identify optimal locations for such crossovers. Minimization of a potential‐energy model for a given structural design demonstrates the importance of local minima. Both deterministic global optimization of a reduced model and multistart optimization of the full model are applied successfully to identify the global minimum. MINLP optimization using a branch‐and‐bound algorithm (GAMS/SBB) identifies an optimal structural design of a DNA tile successfully with significant reduction in computational load compared to exhaustive enumeration, which demonstrates the potential of the proposed method to reduce trial‐and‐error efforts for structural design of DNA tiles. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1804–1817, 2017     

A property‐based approach to the synthesis of material conservation networks with economic and environmental objectives

This article presents a multiobjective optimization model for the recycle and reuse networks based on properties while accounting for the environmental implications of the discharged wastes using life‐cycle assessment. The economic objective function considers fresh sources and treatment costs, whereas the environmental objective function is measured through the eco‐indicator 99. The model considers constraints in the process sinks as well as in the environment based on stream properties such as pH, chemical oxygen demand, toxicity, density, and color, in addition to the composition of the waste streams. A global optimization procedure is developed by indirectly tackling properties through property operators and by segregating the process streams before treatment. Three examples are included, and the results show that it is possible to consider simultaneously the trade‐offs between the total annual costs and the overall environmental impact using the proposed methodology. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Multiway continuous hidden Markov model‐based approach for fault detection and diagnosis

A fault detection and classification scheme that uses probabilistic inference based on multiway continuous hidden Markov models (MCHMM) which is capable of capturing complex system dynamics and uncertainty is proposed. A set of observations from normal and faulty runs of the system was collected and used to generate the training dataset. The training data is assumed to follow a finite Gaussian mixture model. The number of mixture components and associated parameters for the optimal Gaussian mixture fit of the observed data was computed subsequently by clustering using the Figueiredo–Jain algorithm for unsupervised learning. The segmental k‐means algorithm was used to compute the HMM parameters. The applicability of the proposed scheme is investigated for the case of an inverted pendulum system and a fluidized catalytic cracker. The monitoring results for the above cases with the proposed scheme was found to be superior to the multiway discrete hidden Markov model (MDHMM) based scheme in terms of the accuracy of fault detection, especially in case of noisy observations. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2035–2047, 2014