Plantwide Control Research Articles

Sustainable Plantwide Optimizing Control for an Acrylic Acid Process.

This paper presents a sustainable control strategy from a plantwide control (PWC) perspective. The proposed strategy is subjected to testing within an operational environment of an acrylic acid plant. To integrate sustainability tools into the plantwide optimizing control (PWOC) formulation, the present proposal suggests the utilization of green chemistry principles. These principles will be incorporated as constraints within the optimization problem. A comparative analysis is conducted between the proposed sustainable PWOC approach and two alternative structures: a PWOC framework that does not take sustainability issues into account and a conventional PWC structure. The findings indicate that the sustainable PWOC demonstrates superior economic performance from a financial standpoint, reaching the highest cumulative profitability(1.6274 × 105 USD), exceeding 11.94% in comparison to the PWOC without sustainability concerns, which reach a cumulative profitability of 1.4330 × 105 USD, and surpassing 13.01% when compared to the decentralized PWC approach, which reach a cumulative profitability of 1.4158 × 105 USD. Additionally, the sustainable PWOC demonstrated a reduced emission impact on the process, with a decrease of 6.17% compared to the unsustainable PWOC and a 9.79% decrease compared to the decentralized approach. This demonstrates that the incorporation of the proposed green chemistry metrics as an explicit component of the formulated PWC problem significantly mitigates the impacts of global warming and human health.

Improved extended empirical wavelet transform for accurate multivariate oscillation detection and characterisation in plant-wide industrial control loops

The conventional extended empirical wavelet transform (EEWT) proposed recently is intended to decompose multivariate signals with clear peaks in power spectra without considering the cases where the signals contain high noise levels. Even when dealing with signals with distinct peaks, the EEWT method can still encounter challenges in properly decomposing the signals. However, plant-wide data from industrial control loops, including controllers’ outputs, process variables, and manipulated variables, are commonly corrupted by high levels of noise, which can be introduced at various stages of data acquisition, transmission, and processing within the control system. To address these limitations and ensure the applicability of the EEWT to real-world industrial data with diverse and challenging characteristics, this paper presents an improved version called the improved extended empirical wavelet transform (IEEWT). The IEEWT incorporates noise-reduced power spectra and detrended fluctuation analysis techniques to enhance the decomposition. The proposed method demonstrates accurate multivariate data decomposition for both simulated and real data sets, surpassing the limitations associated with the conventional EEWT.

Challenges and opportunities for process intensification in Europe from a process systems engineering perspective

Process Intensification (PI) is an effective way to enhance process efficiency and sustainability at affordable costs and efforts, attracting particular interest in the European area, as one of the most important chemical production areas in the world. PI primarily contributes by developing and testing new processing technologies that once integrated within a process improve the overall process performance substantially but as a result, it may alter the overall process (flowsheet) structure and its dynamic behavior. As such PI plays a key role in improving energy efficiency, optimizing resource allocation, and reducing environmental impact of industrial processes, and thereby leading to a cost-effective, eco-efficient, low-carbon and sustainable industry. However, along with opportunities, the PI new technologies have challenges related to failures in longer-term performance. In this respect, Process Systems Engineering (PSE) stance is more on integration aspects of new PI technologies into processes by making process (re)designs, doing operability studies, and performance optimizations within a supply chain setting. PSE contributes to overcoming the challenges by providing systematic approaches for the design and optimization of PI technologies. This perspective paper is a lightly referenced scholarly opinion piece about the status and directions of process intensification field from a PSE viewpoint. Primarily, it focuses on PSE perspectives towards sustainable lower energy usage process systems and provides a brief overview of the current situation in Europe. It also emphasizes the key challenges and opportunities for (new) PI technologies considering their integration in a process in terms of process synthesis and design, process flowsheet optimization, process and plantwide control, (green) electrification, sustainability improvements. Potential research directions on these aspects are given from an industrial and academic perspective of the authors.

Effect of Change in Plant Capacity towards Controllability of Biohydrogen Production Plant from Biomass

This paper discusses the plant-wide control system of a biohydrogen plant from biomass. The plant’s control structure is designed by assessing the use of model predictive control (MPC) and proportional-integral (PI) controllers for each controlled variable. Then, the control structure design is tested by set point and disturbance change tests, and its performance is evaluated by integral square error (ISE). This plant is simulated in two different capacities to understand the effect of change in plant capacity on the plant’s controllability. Results show that the plant with capacity A has controllability of +25% and −5%, while the plant with capacity B has controllability of +100% and −5% for the dry biomass molar flow rate change test. Both plants have the same controllability of +100% and −100% for the dry biomass temperature test. Both plants result in a dry biomass conversion rate of 16.52%, where the amount of H2 produced is 382 kg/h and 2,291 kg/h, respectively.

Advanced plant-wide control design tools applied to the fluid catalytic cracker-fractionator benchmark

In this work two advanced multivariable control structure design tools are applied on the recently appeared fluid catalytic cracking (FCC)-fractionator benchmark process. Indeed, the partial least squares-based sum of squared deviations (PLS-based SSD) approach and the control allocation (CA) with measurement combination procedure are proposed here to develop new control policies. The minimally invasive feature of PLS-based SSD, allows to obtain an open-loop steady-state model of the process by using closed-loop normal data and to perform a performance/feasibility assessment of the already installed control structure. In this regard, the procedure identified some global integrity problems of the industrial-based control structure proposed in previous works and, therefore, new plant-wide control structures (with different sizing and input/output pairings) were proposed, where the SSD indexes have improved in the range of 50% to 90% and the matrix conditioning guaranteed. The proposed control structures were dynamically tested under typical set point and disturbance variations.

Plantwide Simulation and Operation of a Methanol to Propylene (MTP) Process

BackgroundMethanol to propylene (MTP) process is considered as one of the most important methods for propylene production. The MTP process consists of four sections: conversion of reactants to products, cooling, compression and products separation. In this process, is utilized from multi-stage fixed bed reactors. Due to high exothermicity of the MTP reactors, and existence of recycle loops, a plant-wide control structure is required to be designed for stable, safe and maximum production of propylene. MethodModeling and simulation of the heterogeneous fixed bed MTP reactors with interphase and intra-particle mass and heat transfer were carried out in Aspen Custom Modeler. Steady-state simulations of the MTP process were carried out in Aspen Plus. A simple control strategy was proposed to achieve maximum propylene production with temperature control of the MTP multi-stage reactors by splitting the cold oxygenate feed and the recycled hydrocarbons among the reactor beds. The performance of the proposed control strategy was tested against several disturbances including change in flowrate and composition of feed, using dynamic simulations by Aspen Plus Dynamics. Significant findingDynamic simulation results revealed that simultaneous control of the split range for the 5th and 6th reactor beds and cascade temperature of other reactor beds, could be a suitable temperature control strategy for the reaction section of the MTP process.

A FAST.Farm and MATLAB/Simulink interface for wind farm control design

Increasing the efficiency of wind farms is important for speeding up the transition from fossil fuels to renewable energy sources. Current wind farm control relies on maximization of power generation of individual turbines. However, research has demonstrated that plant-wide wind farm control could optimize the performance of a wind farm. Wind farm simulation tools are crucial in designing, testing, and validating wind farm controls. FAST.Farm is a recently developed multi-physics engineering tool for modeling wind farm performance by solving the aero-hydro-servoelastic dynamics of each individual turbine. The capabilities of FAST.Farm for control design purposes can be extended through a co-simulation with MATLAB/Simulink. Therefore, a MATLAB/Simulink interface with FAST.Farm has been developed. The creation and operation of this interface are explained in this paper. This interface supports developing and testing advanced closed-loop control at the wind turbine and wind farm levels.

Plant‐wide control for processes with recycle and narrow feasible sets

AbstractRecycling mass and energy is a feature in almost all plants and industries. It is reported as an inevitable characteristic in actual industries due to differences between the designed processes and implementation. When a process is set, engineers identify inefficiencies in the planned performance and utilize recycles to try to reduce this difference. However, although recycles help in improving a plant's overall performance compared with a serial arrangement, its effect on dynamic plant behaviour is seldom considered, and these changes begin to impact the features of processes like controllability and stability. Similarly, even though recycles affect the feasible region of individual equipment, its effect is not verified. Based on the aforementioned background, several studies have demonstrated that advanced control strategies allow for the improvement of the dynamic behaviour of complex plants. Different techniques, such as model‐based predictive control and plant‐wide control (PWC), also show that their implementation reduces the effects of recycles. However, there is no explicit methodology to design these controllers and address the impacts of recycles on dynamic behaviour and feasibility. Additionally, there is no tuning procedure to design controllers that optimize the dynamic performance of plants, including the control system. In this context, this work presents an analysis to characterize the effects of recycles on the dynamic behaviour of plants, including how they affect the feasible operating region. Based on the previous analysis, a control methodology is also proposed to design the PWC strategy, explicitly addressing the dynamical phenomena of recycles in processes.

Design and control of an energy-efficient intensified process for biobutanol purification from isopropanol-butanol-ethanol (IBE) fermentation broth

Biobutanol is one of the most preferable alternative fuels because of its superior fuel properties compared to other biofuels. In recent years, biobutanol production through isopropanol (IPA)-butanol-ethanol (IBE) fermentation has gotten more attention than acetone-butanol-ethanol (ABE) fermentation. However, the downstream separation process of this production is very energy intensive due to the several azeotropes. In this study, an energy-efficient process configuration including a reactive distillation column is proposed to break the azeotropes. In the proposed configuration, water present in the azeotropic mixture is consumed by a water-ethylene oxide (EO) reaction. Steady-state results show that the proposed configuration provides a 30% decrease in energy consumption and a 26.7% reduction in total annual cost (TAC) compared to the configurations in the literature, while ethylene glycol is obtained as a by-product besides the biobutanol of 99.5% purity. Additionally, two plantwide control structures are developed, and the dynamic controllability of the configuration is examined. Dynamic results illustrate that a robust control is provided with the proposed control structure 2 (CS2) when it is tested against disturbances in feed flowrate and feed composition.

A novel “feed-backward” control structure for on-demand control of distillation column sequences

The typical conventional plantwide control structure sets the flow rate of the fresh feed entering the process. Sometimes marketing or safety requirements dictate that the flowrate of the product leaving the process must be flow controlled. This structure is called “on-demand.” Important examples of chemicals that must be supplied on an “as-needed” basis include hydrogen, oxygen, synthesis gas, toxic hydrogen cyanide and explosive nitrotoluene. Since the output flow is fixed in an on-demand control structure, the material balance loops controlling liquid levels and gas pressures must work their way back upstream through the process to eventually adjust the fresh feed streams. A novel on-demand control structure is developed in this paper that reduces peak dynamic disturbances throughout the process by immediately changing upstream flow rates in a “feed-backward” manner when the product flow rate is changed.

A plantwide control proposal for ethylbenzene production

In the present article, a plantwide control structure is proposed for the benzene alkylation process. The mathematical model used in the simulations developed in scilab free software, version 6.1, was based on previous studies. Initially, operational restrictions were evaluated and possible controlled variables were chosen based on operational objectives and tested by performing step disorders to validate the criteria. The choice of the best pairs was made through the RGA (Relative Gain Array) relative gain matrix. The structural decisions adopted and the results obtained through computational simulation indicated that although there are interactions between the control loops, the possible pairs, T1-FR2 and T4-Q4, minimize the system interactions. The adjusted parameters of the controller were effective in controlling the disturbance, presenting little or no overshoot with satisfactory ascent and response times. The control structure adopted with the PID controller is just one of the fundamental steps within the plantwide control methodology and shows the controller’s ability to meet the proposed structural objectives.

Methodology for fast development of digital solutions in integrated continuous downstream processing.

The methodology for production of biologics is going through a paradigm shift from batch-wise operation to continuous production. Lot of efforts are focused on integration, intensification, and continuous operation for decreased foot-print, material, equipment, and increased productivity and product quality. These integrated continuous processes with on-line analytics become complex processes, which requires automation, monitoring, and control of the operation, even unmanned or remote, which means bioprocesses with high level of automation or even autonomous capabilities. The development of these digital solutions becomes an important part of the process development and needs to be assessed early in the development chain. This work discusses a platform that allows fast development, advanced studies, and validation of digital solutions for integrated continuous downstream processes. It uses an open, flexible, and extendable real-time supervisory controller, called Orbit, developed in Python. Orbit makes it possible to communicate with a set of different physical setups and on the same time perform real-time execution. Integrated continuous processing often implies parallel operation of several setups and network of Orbit controllers makes it possible to synchronize complex process system. Data handling, storage, and analysis are important properties for handling heterogeneous and asynchronous data generated in complex downstream systems. Digital twin applications, such as advanced model-based and plant-wide monitoring and control, are exemplified using computational extensions in Orbit, exploiting data and models. Examples of novel digital solutions in integrated downstream processes are automatic operation parameter optimization, Kalman filter monitoring, and model-based batch-to-batch control.

Dynamic Modeling and Plantwide Control of a Production Process for Biodiesel and Glycerol

Biodiesel production comprises several interconnected process steps with complex dynamic behavior. Dynamic plant modeling and simulation enhance process understanding and enable model-based control. We present a modular and rigorous dynamic model for biodiesel and glycerol production by alkali-catalyzed oil transesterification. We share the implemented model in Modelica open-source. Moreover, we implement two plantwide control (PWC) structures, where we assume the availability of information-rich measurements in the first and only conventional measurements in the second. To study the process dynamic behavior, we investigate several disturbance scenarios, demonstrating the importance of the plantwide perspective. The PWC structure based on an information-rich configuration shows satisfactory control performance, whereas the other fails to always satisfy product quality requirements, underlining the importance of developing dynamic models for advanced control and estimation techniques. Moreover, the model can be used as a digital twin for industrial plants as well as for model-based control and estimation applications.

Shared communication for coordinated large-scale reinforcement learning control

Deep Reinforcement Learning (DRL) recently emerged as a possibility to control complex systems without the need to model them mathematically. In contrast to classical controllers, DRL alleviates the need for constant parameter tuning, tedious design of control laws, and re-identification procedures in the event of performance degradation. However, the application of DRL algorithms remains fairly modest, and they have not yet established a significant position in process industries. One major obstacle has been their sample inefficiency when facing tasks featuring large state-action spaces. In this work, we show that it is possible to use DRL for plant-wide control by decentralizing and coordinating reinforcement learning. Namely, we express the global policy as a collection of local policies. Every local policy receives local observations and is responsible for controlling a different region of the environment. To enable coordination among local policies, we present a mechanism based on message passing. Messages are encoded by a shared communication channel, which is equipped with a model-based stream to capture the dynamics of the system and enable effective pre-training. The proposed method is evaluated on a set of robotic tasks and a large-scale vinyl acetate monomer (VAM) plant. Experimental results highlight that the proposed model exhibits drastic improvements over baselines in terms of mean scores and sample efficiency.

Plantwide control design using latent variables: An integration between control allocation and a measurement combination approach

This paper presents a plantwide control design methodology based on a novel structure which consists of a decentralized strategy complemented by a control allocation (CA) module and a measurement combination (MC) block. Taking into account a principal components analysis (PCA) selection approach, the CA and MC modules perform a dimensional reduction of the original input–output variable space in order to obtain sets of latent variables (or principal components) as control actions and controlled variables. The use of principal components in the controller design provides several interesting features given that: (i) the conditioning of the subsystem to be controlled can be improved, (ii) when performing combinations of variables, the CA and MC modules act as steady-state decouplers and thus an apparently diagonal process is obtained, which favors the reduction of the variables interaction and the pairing problem is automatically solved, and (iii) they allow to naturally handle nonsquare systems. The proposed design procedure is implemented through a multiobjective bilevel mixed-integer nonlinear programming (BMINLP) optimization problem. The leader problem is based on the minimization of three functional costs: 1- the well-known sum of squared deviations (SSD) index, 2- the number of selected manipulated variables (actuators), and 3- the number of selected measurements (sensors). The inner optimization minimizes the relative gain array number (RGAN). This provides a good trade-off between the degree of conditioning/controllability and the complexity/cost of the resulting system. This problem is efficiently solved through genetic algorithms and allows to perform: (i) the selection of the manipulated variables (actuators) and the measurements (sensors) to be used, (ii) the computation of the matrices that characterize the CA and MC modules, and (iii) the stability analysis of the multivariable control structure. The overall design procedure only requires steady-state models of the process. The Tennessee Eastman case study is considered for the simulation and performance evaluation of the proposed solutions.

Modeling-on-demand-based multivariable control performance monitoring

The current work presents a modeling-on-demand-based multivariable control performance monitoring (CPM) strategy. The suggested approach addresses several challenging topics in multi input-multi output CPM area such as data-driven characteristics, recursive/adaptive philosophy, modeling, plant-wide control (PWC) performance/feasibility indicator, simple structure, minimum interference with the process operation and recovering actions suggestions. The new model-on-demand (MoD) algorithm combines partial least squares (block-wise and moving-window), model quality index, and the reference matrix concepts to improve the robustness characteristics of the recursive process, i.e. the model adaptation is only performed when it is needed. Recovery actions may involve changes on tuning parameters as well as control structure modifications. The proposed strategy allows to identify static and dynamic abnormal events in the process which could produce strong degradation of the PWC performance. The robustness of the proposed methodology is tested by dynamic simulations on the well-known Shell fractionator process and compared with some classical recursive algorithms.

Economic plantwide control of a hybrid solid oxide fuel cell - gas turbine system

This study presents a novel approach for hybrid solid oxide fuel cell (SOFC) - gas turbine (GT) system control. The approach is based on a plantwide top-down strategy which leads to a proper selection of the control structure. As a result, while maintaining the system stability and dynamic performance during transients, the system operation is also kept at optimal conditions using simple PI controllers. The optimization cost function is defined to be the unit price of the net produced electricity. The system inlet fuel flow and the power drawn from the SOFC are considered as the main system disturbances. The two-dimensional disturbance space is discretized. Rigorous optimization problems are performed in each disturbance node and variation of system efficiency and constraint values are evaluated. Three different operating regions, with different sets of active constraints, are characterized. Equivalent to the identified operating regions, an efficiency map is also provided which illustrates the achievable system efficiencies in presence of disturbances. Based on the optimization outcomes, the identification of appropriate controlled variables is accomplished in each region. It revealed that to keep near-optimal operation under various operating conditions; the combustion chamber outlet temperature should be treated as a key controlled variable. Finally, dynamic simulations are performed and the proficiency of the proposed control structure is justified. According to the results, maximum SOFC temperature transients in all regions would be less than 2 K.min-1 while variations on the hybrid system output power are kept below 3 % and efficiency loss is restricted to 2 %.

A control philosophy for complex Non-square chemical process

All the complex non-square chemical processes may have thousands of measurement and control loops and these loops should be paired properly. A term control philosophy decides the structural decisions of the overall plant instead of the term plantwide control which is not meant for tuning each of these loops. Operational safety require direct inclusion of control system into associated safety to handle disturbances and failure in critical devices of a chemical process. Though most of the process units are non-linear and multivariable in nature, their operations are based on linear controllers to provide overall stability and safe operations. As the design of an appropriate control structure for complex non-square chemical processes are difficult, it is very challenging to control these processes. Non-square systems offers safe operational procedures and ensures plant safety. The proposed method of control configuration selection for non-square Multi Input Multi Output (MIMO) system is an extension of square MIMO system. An appropriate selection of control configuration for non-square chemical processes have been addressed in this paper. The proposed method of dynamic loop interactions is based on the computation and comparison of areas under responses for all possible combinations of manipulated and controlled variables. The control configurations that emerged from subsequent analyses are able to provide adequate inherent safety to the overall process in a closed-loop sense. This paper assesses the interaction among the loops having unequal number of input and output variables. This procedure is illustrated via simulation of examples taken for both the under-defined and over-defined non–square MIMO system. Also the quantitative measure of process safety index is obtained for the process.

Plantwide control of a biodiesel production process with variable feedstock

Biodiesel can be obtained from a variety of oils and fats rich in triglycerides. Due to this variability, production processes must be flexible enough to handle different raw materials. This work reports the simulation-based design of a biodiesel production process able to obtain the biofuel from different raw materials. This approach avoids dependence on a single kind of raw material by developing a flexible process. The process is designed to ensure that the product accomplishes the international standard EN 14214 for any of the tested raw materials. A plantwide control structure is then developed to handle the potential variations in feed composition. The controllers’ parameters are tuned, and the control scheme is tested to determine its capability to reach a new steady state in an acceptable period. According to the results, the steady state design allows producing biodiesel from three different raw materials in a single process by only adjusting the operational conditions, obtaining the lowest total annual cost when processing microalgae oil. Moreover, the proposed control strategy allows rejecting the perturbations occurring when the change of raw material occurs, stabilizing after a maximum period of 8 h while maintaining a product still accomplishing the international standard EN 14214.

Dynamic process simulations and control of a lignite/waste to Fischer Tropsch liquids plant

This study comprises the dynamic process simulations of a HTW gasification plant for Fischer-Tropsch (FT) liquids production under transient feedstock of lignite and solid recovered fuel (SRF) as well as load variations. Main objective of the current study is the proof of concept in unsteady conditions and the effective plant wide control of a novel gasification-driven liquid fuels production pathway. Therefore, the dynamic analysis of all sub-units in an integrated simulation scheme by means of Aspen Plus Dynamics (APD) and MATLAB/Simulink interference is attempted. In this way, it is possible to evaluate the impact of system disturbances on every process stage and apply the required control strategies to ensure system stability. Another aspect addressed within this work is the alternative management of solid materials in APD environment, which is an inherent drawback of this otherwise appropriate software as mentioned in previous studies. The performed dynamic simulations revealed that the proposed value chain seems to have a respectable potential in terms of operational flexibility and performance stability, always in relation to the gasification-based chemical plants and the constraints that accompany them.