A data‐driven rolling‐horizon online scheduling model for diesel production of a real‐world refinery

A rolling‐horizon optimal control strategy is developed to solve the online scheduling problem for a real‐world refinery diesel production based on a data‐driven model. A mixed‐integer nonlinear programming (MINLP) scheduling model considering the implementation of nonlinear blending quality relations and quantity conservation principles is developed. The data variations which drive the MINLP model come from different sources of certain and uncertain events. The scheduling time horizon is divided into equivalent discrete time intervals, which describe regular production and continuous time intervals which represent the beginning and ending time of expected and unexpected events that are not restricted to the boundaries of discrete time intervals. This rolling‐horizon optimal control strategy ensures the dimension of the diesel online scheduling model can be accepted in industry use. LINGO is selected to be the solution software. Finally, the daily diesel scheduling scheme of one entire month for a real‐world refinery is effectively solved. © 2012 American Institute of Chemical Engineers AIChE J, 59: 1160–1174, 2013     

Lyapunov‐based MPC with robust moving horizon estimation and its triggered implementation

In this work, we consider moving horizon state estimation (MHE)‐based model predictive control (MPC) of nonlinear systems. Specifically, we consider the Lyapunov‐based MPC (LMPC) developed in (Mhaskar et al., IEEE Trans Autom Control. 2005;50:1670–1680; Syst Control Lett. 2006;55:650–659) and the robust MHE (RMHE) developed in (Liu J, Chem Eng Sci. 2013;93:376–386). First, we focus on the case that the RMHE and the LMPC are evaluated every sampling time. An estimate of the stability region of the output control system is first established; and then sufficient conditions under which the closed‐loop system is guaranteed to be stable are derived. Subsequently, we propose a triggered implementation strategy for the RMHE‐based LMPC to reduce its computational load. The triggering condition is designed based on measurements of the output and its time derivatives. To ensure the closed‐loop stability, the formulations of the RMHE and the LMPC are also modified accordingly to account for the potential open‐loop operation. A chemical process is used to illustrate the proposed approaches. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4273–4286, 2013     

Simultaneous design of explicit/multi-parametric constrained moving horizon estimation and robust model predictive control

In this work we present a rigorous methodology for the simultaneous design of moving horizon estimation (MHE) and robust model predictive control based on multi-parametric programming. First, an explicit/multi-parametric solution of the MHE is derived. Then, a novel method is presented that allows for the derivation of the estimation error dynamics, the bounding set of the estimation error, and the state estimate dynamic equations of constrained MHE. A framework is then presented for the design of robust explicit/multi-parametric model predictive control (MPC) controllers, based on tube-based MPC methods, which ensures that no constraints are violated due to the estimation error and the process noise in the system. This framework is first shown for the Kalman filter and unconstrained MHE and is then extended to the constrained MHE.     

Model‐predictive safety system for proactive detection of operation hazards

A method of designing model‐predictive safety systems that can detect operation hazards proactively is presented. Such a proactive safety system has two major components: a set of operability constraints and a robust state estimator. The safety system triggers alarm(s) in real time when the process is unable to satisfy an operability constraint over a receding time‐horizon into the future. In other words, the system uses a process model to project the process operability status and to generate alarm signals indicating the presence of a present or future operation hazard. Unlike typical existing safety systems, it systematically accounts for nonlinearities and interactions among process variables to generate alarm signals; it provides alarm signals tied to unmeasurable, but detectable, state variables; and it generates alarm signals before an actual operation hazard occurs. The application and performance of the method are shown using a polymerization reactor example. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2024–2042, 2016     

Model‐based analysis of chemical‐looping combustion experiments. Part II: Optimal design of CH4‐NiO reduction experiments

There is significant controversy in the reduction kinetics of chemical‐looping combustion (CLC) between NiO and CH₄. We propose an application of a model‐based framework to improve the quality of CLC experiments with respect to model discrimination and parameter estimation. First, optimal experiments are designed and executed to reject inadequate models and to determine a true model structure for the reaction kinetics of the CH₄‐NiO system. Then, kinetics with statistical significance is estimated from experiments aimed at reducing parameter uncertainty. To maximize the observability of the NiO reduction reactions, fixed bed experiments should exhibit a peak separation of the concentration profiles, an initial high methane slip, and low overall CO₂ selectivity. Several case studies are presented to check the adequacy of the recommended model and evaluate its predictive ability and extrapolation capabilities. The model resulting from this work is validated and suitable for application in process design and optimization. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2432–2446, 2016     

Optimal design of batch‐storage network considering ownership

This article develops a model of multi‐national supply chain activities, which incorporates currency storage units to manage currency flows associated with activities such as raw material procurement, processing, inventory control, transportation, and finished product sales. The core contribution of this model is that it facilitates the quantitative investigation of the influence of macroscopic economic factors such as ownership on supply chain operational decisions. The supply chain system is modeled as a batch‐storage network with recycle streams. The supply chain optimization problem is posed with the objective of minimizing the opportunity costs of annualized capital investments and currency/material inventories, while taking into account the benefit to stockholders in the numeraire currency. The major constraints on the optimization are that the material and currency storage units must not be depleted. A production and inventory analysis formulation (the periodic square wave model) provides useful expressions for the upper and lower bounds and for the average levels of the currency and material inventory holdings. The expressions for the Kuhn‐Tucker conditions of the optimization problem are reduced to a subproblem that allows development of analytical lot‐sizing equations. The lot sizes of procurement, production, transportation, and financial transactions can be determined in closed form once the average flow rates are known. The key result we obtain is that optimal value of the economic order quantity changes substantially with variation in ownership, thus showing quantitatively that ownership structure does impact plant operation. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2418–2425, 2018     

Wavelet change‐point estimation for long memory non‐parametric random design models

For a random design regression model with long memory design and long memory errors, we consider the problem of detecting a change point for sharp cusp or jump discontinuity in the regression function. Using the wavelet methods, we obtain estimators for the change point, the jump size and the regression function. The strong consistencies of these estimators are given in terms of convergence rates.     

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     

Generalization of a parameter set selection procedure based on orthogonal projections and the D‐optimality criterion

Many models derived from first principles contain more parameters than can be reliably estimated from data. Selecting a subset of the parameters for estimation is one common approach to deal with this problem. One popular method sequentially selects parameters based on orthogonalization of the sensitivity vectors; however, it has the drawback that only one parameter is added at each step of the iteration and that no correlations of not yet chosen parameters can be taken into account. To address this drawback, a generalization of the parameter set selection procedure based on orthogonalization is presented in this work. The procedure can add any number of parameters at each iteration such that correlations among the parameters that will be added to the set of estimated parameters can be taken into account. It is shown that two existing parameter set selection techniques form special cases of the presented method. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Experimental design for the joint model discrimination and precise parameter estimation through information measures

Experimental design procedures for model discrimination and for estimation of precise model parameters are usually treated as independent techniques. In order to conciliate the objectives of both experimental design procedures, the present paper proposes the use of experimental design criteria that are based on measures of the information gain when new experiments are carried out. The proposed criterion depends on the volumes of the confidence regions of the model parameters and presents a number of advantageous aspects, such as the conciliation of the usual experimental design objectives and the fact that the obtained criterion values can be easily interpreted in terms of the information eliminated after carrying out additional experiments. Besides, the proposed design criterion can easily accommodate multiobjective experimental design approaches, as shown in the examples.     

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     

药物存储用热电除湿装置性能实验及参数优化

热电冷却除湿是一种新型节能、环保的除湿技术。鉴于目前热电除湿装置除湿速率和效率较低,采用两片TEC-12705型半导体制冷片进行对角布置,设计了一套热电除湿装置,对影响其性能的因素做了系统的分析并针对实际环境工况提出相应的设计和优化理念。结果表明,风速、热电制冷片驱动电压、气流组织形式都对热电除湿装置性能有一定的影响,确定风速时应综合考虑空气与冷端热沉换热能力和空气与冷端热沉接触时间。     

Conceptual design of ammonia‐based energy storage system: System design and time‐invariant performance

Chemicals‐based energy storage is promising for integrating intermittent renewables on the utility scale. High round‐trip efficiency, low cost, and considerable flexibility are desirable. To this end, an ammonia‐based energy storage system is proposed. It utilizes a pressurized reversible solid‐oxide fuel cell for power conversion, coupled with external ammonia synthesis and decomposition processes and a steam power cycle. A coupled refrigeration cycle is utilized to recycle nitrogen completely. Pure oxygen, produced as a side‐product in electrochemical water splitting, is used to drive the fuel cell. A first‐principle process model extended by detailed cost calculation is used for process optimization. In this work, the performance of a 100 MW system under time‐invariant operation is studied. The system can achieve a round‐trip efficiency as high as 72%. The lowest levelized cost of delivered energy is obtained at 0.24 $/kWh, which is comparable to that of pumped hydro and compressed air energy storage systems. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1620–1637, 2017     

Kinetic model‐aided reactor design for peroxidase‐catalyzed removal of phenol in the presence of polyethylene glycol

A batch‐mixed reactor and a plug‐flow reactor were shown to have identical reaction kinetics for peroxidase‐catalyzed removal of phenol. Semi‐batch operation and step‐addition operation of the plug‐flow reactor with respect to hydrogen peroxide produced similar output. In the presence of polyethylene glycol, a continuous stirred tank reactor needed a long time to complete the reaction at the minimum enzyme dose. It was not necessary to stir the reaction mixture continuously during the reaction, hence polymerization and settling of products happened simultaneously. Recycling of the precipitates was not beneficial either for the removal efficiency or for the operation of a settling tank. A model previously developed to simulate the reaction in a batch reactor was used to predict the process in different types of reactor system. A plug‐flow reactor system is recommended for the reaction in the presence of polyethylene glycol; the system consisting of a small mixing tank followed by a tank in which reaction and settling occur simultaneously. © 1999 Society of Chemical Industry     

Computer‐aided design of tailor‐made ionic liquids

Ionic liquids (IL), with their negligible vapor pressure, have the potential to replace volatile organic solvents in several processes. They also exhibit other unique characteristics, such as high thermal stability, wide liquid range, and wide electrochemical window, which make them attractive for many important applications. In addition, millions of ILs can be formed through different combination of cations, anions, and other functional groups. Till now, majority of work on IL selection, for a given application, is guided by trial and error experimentation. In this article, we present a computer‐aided IL design framework, based on semiempirical structure‐property models and optimization methods, which can consider several IL candidates and design optimal structures for a given application. This powerful methodology has great potential to act as a knowledge‐based framework to aid synthetic chemists and engineers develop new ILs. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4627–4640, 2013     

Simulation,model‐reduction,and state estimation of a two‐component coagulation process

The issue of state estimation of an aggregation process through (1) using model reduction to obtain a tractable approximation of the governing dynamics and (2) designing a fast moving‐horizon estimator for the reduced‐order model is addressed. The method of moments is first used to reduce the governing integro‐differential equation down to a nonlinear ordinary differential equation. This reduced‐order model is then simulated for both batch and continuous processes and the results are shown to agree with constant Number Monte Carlo simulation results of the original model. Next, the states of the reduced order model are estimated in a moving horizon estimation approach. For this purpose, Carleman linearization is first employed and the nonlinear system is represented in a bilinear form. This representation lessens the computation burden of the estimation problem by allowing for analytical solution of the state variables as well as sensitivities with respect to decision variables. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1557–1567, 2016