Moving horizon closed‐loop production scheduling using dynamic process models

The economic circumstances that define the operation of chemical processes (e.g., product demand, feedstock and energy prices) are increasingly variable. To maximize profit, changes in production rate and product grade must be scheduled with increased frequency. To do so, process dynamics must be considered in production scheduling calculations, and schedules should be recomputed when updated economic information becomes available. In this article, this need is addressed by introducing a novel moving horizon closed‐loop scheduling approach. Process dynamics are represented explicitly in the scheduling calculation via low‐order models of the closed‐loop dynamics of scheduling‐relevant variables, and a feedback connection is built based on these variables using an observer structure to update model states. The feedback rescheduling mechanism consists of, (a) periodic schedule updates that reflect updated price and demand forecasts, and, (b) event‐driven updates that account for process and market disturbances. The theoretical developments are demonstrated on the model of an industrial‐scale air separation unit. © 2016 American Institute of Chemical Engineers AIChE J, 63: 639–651, 2017     

Dynamic real‐time optimization with closed‐loop prediction

Process plants are operating in an increasingly global and dynamic environment, motivating the development of dynamic real‐time optimization (DRTO) systems to account for transient behavior in the determination of economically optimal operating policies. This article considers optimization of closed‐loop response dynamics at the DRTO level in a two‐layer architecture, with constrained model predictive control (MPC) applied at the regulatory control level. A simultaneous solution approach is applied to the multilevel DRTO optimization problem, in which the convex MPC optimization subproblems are replaced by their necessary and sufficient Karush–Kuhn–Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints. The performance of the closed‐loop DRTO strategy is compared to that of the open‐loop prediction counterpart through a multi‐part case study that considers linear dynamic systems with different characteristics. The performance of the proposed strategy is further demonstrated through application to a nonlinear polymerization reactor grade transition problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3896–3911, 2017     

Input reconstruction for statistical‐based fault detection and isolation

In this work, an input reconstruction scheme for detecting and isolating sensor, actuator, and process faults is proposed. The scheme uses model‐based and statistical‐based FDI methods, which yields an improved analysis of abnormal operation conditions in chemical processes. The main advantage of the proposed approach over existing works lies in the reconstruction of system inputs and the subsequent estimation of fault signatures. This advantage is demonstrated through simulation examples and the analysis of recorded process data from a reactive batch distillation column. © 2011 American Institute of Chemical Engineers AIChE J, 2012     

Quality‐relevant and process‐relevant fault monitoring with concurrent projection to latent structures

This paper proposes a new concurrent projection to latent structures is proposed in this paper for the monitoring of output‐relevant faults that affect the quality and input‐relevant process faults. The input and output data spaces are concurrently projected to five subspaces, a joint input‐output subspace that captures covariations between input and output, an output‐principal subspace, an output‐residual subspace, an input‐principal subspace, and an input‐residual subspace. Fault detection indices are developed based on these subspaces for various fault detection alarms. The proposed monitoring method offers complete monitoring of faults that happen in the predictable output subspace and the unpredictable output residual subspace, as well as faults that affect the input spaces only. Numerical simulation examples and the Tennessee Eastman challenge problem are used to illustrate the effectiveness of the proposed method. © 2012 American Institute of Chemical Engineers AIChE J, 59: 496–504, 2013     

Integration of scheduling and control for batch processes using multi‐parametric model predictive control

Integration of scheduling and control results in Mixed Integer Nonlinear Programming (MINLP) which is computationally expensive. The online implementation of integrated scheduling and control requires repetitively solving the resulting MINLP at each time interval. (Zhuge and Ierapetritou, Ind Eng Chem Res. 2012;51:8550–8565) To address the online computation burden, we incorporare multi‐parametric Model Predictive Control (mp‐MPC) in the integration of scheduling and control. The proposed methodology involves the development of an integrated model using continuous‐time event‐point formulation for the scheduling level and the derived constraints from explicit MPC for the control level. Results of case studies of batch processes prove that the proposed approach guarantees efficient computation and thus facilitates the online implementation. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3169–3183, 2014     

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     

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     

Data‐based linear Gaussian state‐space model for dynamic process monitoring

This article develops a data‐based linear Gaussian state‐space model for monitoring of dynamic processes under noisy environment. The Kalman filter is introduced for construction of the linear Gaussian state‐space model, and an iterative expectation‐maximization algorithm is used for model parameters learning. With the incorporation of the dynamic data information, a new fault detection and identification approach is proposed. The feasibility and effectiveness of the two monitoring statistics in the new method are theoretically evaluated and further confirmed through two case studies. Furthermore, detailed fault smearing effect analysis of the proposed method is provided and compared with other identification methods. Based on the simulation results of two case studies, the superiority of the proposed method is explored. © 2012 American Institute of Chemical Engineers AIChE J, 2012     

Process control and monitoring of reactive crystallization of L‐glutamic acid

The aim of the present study was to investigate feedback control of a reactive crystallization process. The present study built up a control structure needed to control the driving force of reactive crystallization using the feed rate of added acid. The concentration of the crystallizing compound and pH was used to compute feedback in the closed‐loop control of semi‐batch precipitation. The concentration of L ‐glutamic acid was determined from measured MID‐IR ATR‐FTIR spectra based on a multivariate model. Dynamic change of set value was based on the mass of added sulfuric acid and pH. The studied properties of the product crystals were polymorphism and crystal size. The polymorphic composition was analyzed with a Raman spectrometer and was expressed by mass fraction of the α‐polymorph. The obtained results showed that the developed feedback process control system allows effective control of forming of polymorphs. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Dynamic analysis and open‐loop start‐up of an integrated radiant syngas cooler and steam methane reformer

The transient performance of an integrated radiant syngas cooler (RSC) of an entrained‐bed gasifier and steam methane reformer (SMR) is investigated. Base‐case designs using either co‐current or counter‐current configurations are subjected to operating transients to evaluate the feasibility to transition to new steady states. Each system, under open loop, is subjected to changes in key variables of the SMR feed on the tube side and disturbances to variables of the coal‐derived syngas on the RSC side to determine the dynamics and stability of the integrated system. The results indicate that the co‐current configuration is flexible to move to new operating steady states and more safe than the counter‐current configuration, although it provides less cooling and has poorer methane conversion. The variables likely to violate the design limit in the event of a disturbance are identified. A start‐up procedure is also established based on industrial practices employed for entrained‐bed gasifiers and methane reformers. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1602–1619, 2017     

A novel adaptive surrogate modeling‐based algorithm for simultaneous optimization of sequential batch process scheduling and dynamic operations

A novel adaptive surrogate modeling‐based algorithm is proposed to solve the integrated scheduling and dynamic optimization problem for sequential batch processes. The integrated optimization problem is formulated as a large scale mixed‐integer nonlinear programming (MINLP) problem. To overcome the computational challenge of solving the integrated MINLP problem, an efficient solution algorithm based on the bilevel structure of the integrated problem is proposed. Because processing times and costs of each batch are the only linking variables between the scheduling and dynamic optimization problems, surrogate models based on piece‐wise linear functions are built for the dynamic optimization problems of each batch. These surrogate models are then updated adaptively, either by adding a new sampling point based on the solution of the previous iteration, or by doubling the upper bound of total processing time for the current surrogate model. The performance of the proposed method is demonstrated through the optimization of a multiproduct sequential batch process with seven units and up to five tasks. The results show that the proposed algorithm leads to a 31% higher profit than the sequential method. The proposed method also outperforms the full space simultaneous method by reducing the computational time by more than four orders of magnitude and returning a 9.59% higher profit. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4191–4209, 2015     

A support vector clustering‐based probabilistic method for unsupervised fault detection and classification of complex chemical processes using unlabeled data

A new support vector clustering (SVC)‐based probabilistic approach is developed for unsupervised chemical process monitoring and fault classification in this article. The spherical centers and radii of different clusters corresponding to normal and various kinds of faulty operations are estimated in the kernel feature space. Then the geometric distance of the monitored samples to different cluster centers and boundary support vectors are computed so that the distance–ratio–based probabilistic‐like index can be further defined. Thus, the most probable clusters can be assigned to the monitored samples for fault detection and classification. The proposed SVC monitoring approach is applied to two test scenarios in the Tennessee Eastman Chemical process and its results are compared to those of the conventional K‐nearest neighbor Fisher discriminant analysis (KNN‐FDA) and K‐nearest neighbor support vector machine (KNN‐SVM) methods. The result comparison demonstrates the superiority of the SVC‐based probabilistic approach over the traditional KNN‐FDA and KNN‐SVM methods in terms of fault detection and classification accuracies. © 2012 American Institute of Chemical Engineers AIChE J, 59: 407–419, 2013     

Theoretical and computational comparison of continuous‐time process scheduling models for adjustable robust optimization

Coping with uncertainty in system parameters is a prominent hurdle when scheduling multi‐purpose batch plants. In this context, our previously introduced multi‐stage adjustable robust optimization (ARO) framework has been shown to obtain more profitable solutions, while maintaining the same level of immunity against risk, as compared to traditional robust optimization approaches. This paper investigates the amenability of existing deterministic continuous‐time scheduling models to serve as the basis of this ARO framework. A comprehensive computational study is conducted that compares the numerical tractability of various models across a suite of literature benchmark instances and a wide range of uncertainty sets. This study also provides, for the first time in the open literature, robust optimal solutions to process scheduling instances that involve uncertainty in production yields. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3055–3070, 2018     

Inventory pinch based,multiscale models for integrated planning and scheduling‐part II: Gasoline blend scheduling

Integration of planning and scheduling optimizes simultaneous decisions at both levels, thereby leading to more efficient operation. A three‐level discrete‐time algorithm which uses nonlinear models and integrates planning and detailed scheduling is introduced: first level optimizes nonlinear blend models via multiperiod nonlinear programming (NLP), where period boundaries are initially determined by the inventory pinch points; second level uses fixed recipes (from the first level) in a multiperiod mixed‐integer linear program to determine first an optimal production plan and then to optimize an approximate schedule which minimizes the total number of switches in blenders and swing tanks; third level computes detailed schedules that adhere to inventory constraints computed in the approximate schedule. If inventory infeasibilities appear at the second or the third level, the first‐level periods are subdivided and blend recipes are reoptimized. Algorithm finds the same or better solutions and is substantially faster than previously published full‐space continuous‐time model. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2475–2497, 2014     

Tightening methods for continuous‐time mixed‐integer programming models for chemical production scheduling

Important advances in modeling chemical production scheduling problems have been made in recent years, yet effective solution methods are still required. We use an algorithm that uses process network and customer demand information to formulate powerful valid inequalities that substantially improve the solution process. In particular, we extend the ideas recently developed for discrete‐time formulations to continuous‐time models and show that these tightening methods lead to a significant decrease in computational time, up to more than three orders of magnitude for some instances. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4461–4467, 2013     

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     

Short‐term scheduling of a large‐scale oil‐refinery operations: Incorporating logistics details

Refineries are increasingly concerned with improving the scheduling of their operations to achieve better economic performances by minimizing quality, quantity, and logistics give away. In this article, we present a comprehensive integrated optimization model based on continuous‐time formulation for the scheduling problem of production units and end‐product blending problem. The model incorporates quantity, quality, and logistics decisions related to real‐life refinery operations. These involve minimum run‐length requirements, fill‐draw‐delay, one‐flow out of blender, sequence‐dependent switchovers, maximum heel quantity, and downgrading of better quality product to lower quality. The logistics giveaways in our work are associated with obtaining a feasible solution while minimizing violations of sequence‐dependent switchovers and maximum heel quantity restrictions. A set of valid inequalities are proposed that improves the computational performance of the model significantly. The formulation is used to address realistic case studies where feasible solutions are obtained in reasonable computational time. © 2010 American Institute of Chemical Engineers AIChE J, 2011