Integration of process design and control: A review

There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities

Optimal Selection of Measurements and Manipulated Variables for Production Control

The main objective in a chemical plant is to improve profit while assuring products meet required specifications and satisfy environmental and operational constraints. A sub-objective that directly affects profit (main objective) is to improve the control performance of key economic variables in the plant, such as production rate and quality. An optimal control-based approach is proposed to determine a set of measurements and manipulated variables (dominant variables) and to structure them to improve plant profitability. This approach is model-based, and it uses optimal control theory to find the dominant variables that affect economic variables in the plant. First, the measurements and manipulated variables that affect product flow and quality are identified. Then, a decentralized control structure is designed to pair these measurements with the manipulated variables. Finally, a model predictive control (MPC) is built on top of the resulting control structure. This is done to manipulate the set point of these loops in order to change the production rate and product quality. Another sub-objective that affects the profit in the plant is to improve the control of inerts. In general, the inventory of the inerts is controlled using a purge. A new methodology to optimally control inerts is presented. This methodology aims to reduce the losses that occur throughout the purge by solving an optimization problem to determine the maximum amount of inert that can be handled in the plant without having shut down of the plant due to inert accumulation. The methodology is successfully applied to the Tennessee Eastman Plant where the operating cost was reduced approximately 4%. This methodology solves an approximation to an optimal economic problem. First, it improves the control performance of key economic variables in the plant. Therefore, tighter control of these economic variables is achieved and the plant can be operated closer to operational constraints. Second, it minimizes purge which is a variable that generally causes significant costs in the plant. This approach is applied to the Tennessee Eastman and the Vinyl Acetate Processes. Results demonstrating the effectiveness of this method are presented and compared with the results from other authors

A model structure-driven hierarchical decentralized stabilizing control structure for process networks

Based on the structure of process models a hierarchically structured state-space model has been proposed for process networks with controlled mass convection and constant physico-chemical properties. Using the theory of cascade-connected nonlinear systems and the properties of Metzler and Hurwitz matrices it is shown that process systems with controlled mass convection and without sources or with stabilizing linear source terms are globally asymptotically stable. The hierarchically structured model gives rise to a distributed controller structure that is in agreement with the traditional hierarchical process control system structure where local controllers are used for mass inventory control and coordinating controllers are used for optimizing the system dynamics. The proposed distributed controller is illustrated on a simple non-isotherm jacketed chemical reactor

Deep Recurrent Neural Networks for Fault Detection and Classification

Deep Learning is one of the fastest growing research topics in process systems engineering due to the ability of deep learning models to represent and predict non-linear behavior in many applications. However, the application of these models in chemical engineering is still in its infancy. Thus, a key goal of this work is assessing the capabilities of deep-learning based models in a chemical engineering applications. The specific focus in the current work is detection and classification of faults in a large industrial plant involving several chemical unit operations. Towards this goal we compare the efficacy of a deep learning based algorithm to other state-of-the-art multivariate statistical based techniques for fault detection and classification. The comparison is conducted using simulated data from a chemical benchmark case study that has been often used to test fault detection algorithms, the Tennessee Eastman Process (TEP). A real time online scheme is proposed in the current work that enhances the detection and classifications of all the faults occurring in the simulation. This is accomplished by formulating a fault-detection model capable of describing the dynamic nonlinear relationships among the output variables and manipulated variables that can be measured in the Tennessee Eastman Process during the occurrence of faults or in the absence of them. In particular, we are focusing on specific faults that cannot be correctly detected and classified by traditional statistical methods nor by simpler Artificial Neural Networks (ANN). To increase the detectability of these faults, a deep Recurrent Neural Network (RNN) is programmed that uses dynamic information of the process along a pre-specified time horizon. In this research we first studied the effect of the number of samples feed into the RNN in order to capture more dynamical information of the faults and showed that accuracy increases with this number e.g. average classification rates were 79.8%, 80.3%, 81% and 84% for the RNN with 5, 15, 25 and 100 number of samples respectively. As well, to increase the classification accuracy of difficult to observe faults we developed a hierarchical structure where faults are grouped into subsets and classified with separate models for each subset. Also, to improve the classification for faults that resulted in responses with low signal to noise ratio excitation was added to the process through an implementation of a pseudo random signal(PRS). By applying the hierarchical structure there is an increment on the signal-to-noise ratio of faults 3 and 9, which translates in an improvement in the classification accuracy in both of these faults by 43.0% and 17.2% respectively for the case of 100 number of samples and by 8.7% and 23.4% for 25 number samples. On the other hand, applying a PRS to excite the system has showed a dramatic increase in the classification rates of the normal state to 88.7% and fault 15 up to 76.4%. Therefore, the proposed method is able to improve considerably both the detection and classification accuracy of several observable faults, as well as faults considered to be unobservable when using other detection algorithms. Overall, the comparison of the deep learning algorithms with Dynamic PCA (Principal Component Analysis) techniques showed a clear superiority of the deep learning techniques in classifying faults in nonlinear dynamic processes. Finally, we develop these same techniques to different operational modes of the TEP simulation, achieving comparable improvements to the classification accuracies

Retrofit self-optimizing control: a step forward towards real implementation

After 15 year development, it is still hard to find any real application of the self-optimizing control (SOC) strategy, although it can achieve optimal or near optimal operation in industrial processes without repetitive realtime optimization. This is partially because of the misunderstanding that the SOC requires to completely reconfigure the entire control system which is generally unacceptable for most process plants in operation, even though the current one may not be optimal. To alleviate this situation, this paper proposes a retrofit SOC methodology aiming to improve the optimality of operation without change of existing control systems. In the new retrofitted SOC systems, the controlled variables (CVs) selected are kept at constant by adjusting setpoints of existing control loops, which therefore constitutes a two layer control architecture. CVs made from measurement combinations are determined to minimise the global average losses. A subset measurement selection problem for the global SOC is solved though a branch and bound algorithm. The standard testbed Tennessee Eastman (TE) process is studied with the proposed retrofit SOC methodology. The optimality of the new retrofit SOC architecture is validated by comparing two state of art control systems by Ricker and Larsson et al., through steady state analysis as well as dynamic simulations

plant-wide control of industrial processes using rigorous simulation and heuristics

Ph.DDOCTOR OF PHILOSOPH

Multivariate Statistical Process Monitoring and Control

Application of statistical methods in monitoring and control of industrially significant processes are generally known as statistical process control (SPC). Since most of the modern day industrial processes are multivariate in nature, multivariate statistical process control (MVSPC), supplanted univariate SPC techniques. MVSPC techniques are not only significant for scholastic pursuit; it has been addressing industrial problems in recent past. . Monitoring and controlling a chemical process is a challenging task because of their multivariate, highly correlated and non-linear nature. Present work based on successful application of chemometric techniques in implementing machine learning algorithms. Two such chemometric techniques; principal component analysis (PCA) & partial least squares (PLS) were extensively adapted in this work for process identification, monitoring & Control. PCA, an unsupervised technique can extract the essential features from a data set by reducing its dimensionality without compromising any valuable information of it. PLS finds the latent variables from the measured data by capturing the largest variance in the data and achieves the maximum correlation between the predictor and response variables even if it is extended to time series data. In the present work, new methodologies; based on clustering time series data and moving window based pattern matching have been proposed for detection of faulty conditions as well as differentiating among various normal operating conditions of Biochemical reactor, Drum-boiler, continuous stirred tank with cooling jacket and the prestigious Tennessee Eastman challenge processes. Both the techniques emancipated encouraging efficiencies in their performances. The physics of data based model identification through PLS, and NNPLS, their advantages over other time series models like ARX, ARMAX, ARMA, were addressed in the present dissertation. For multivariable processes, the PLS based controllers offered the opportunity to be designed as a series of decoupled SISO controllers. For controlling non-linear complex processes neural network based PLS (NNPLS) controllers were proposed. Neural network; a supervised category of data based modeling technique was used for identification of process dynamics. Neural nets trained with inverse dynamics of the process or direct inverse neural networks (DINN) acted as controllers. Latent variable based DINNS’ embedded in PLS framework termed as NNPLS controllers. (2×2), (3×3), and (4×4) Distillation processes were taken up to implement the proposed control strategy followed by the evaluation of their closed loop performances. The subject plant wide control deals with the inter unit interactions in a plant by the proper selection of manipulated and measured variables, selection of proper control strategies. Model based Direct synthesis and DINN controllers were incorporated for controlling brix concentrations in a multiple effect evaporation process plant and their performances were compared both in servo and regulator mode

Effective Fault Diagnosis in Chemical Plants By Integrating Multiple Methodologies

Ph.DDOCTOR OF PHILOSOPH