Optimal Process Control Research Articles

Hybrid evolutionary robust optimization-based optimal control for time-delay nonlinear systems

Optimal controls receive much attention owing to their remarkable performance for nonlinear systems. However, unknown time-delay disturbances in the optimal control process make it difficult to find high-quality optimal set points. To address this problem, a hybrid evolutionary robust optimization-based optimal control (HERO-OC) method is proposed to reduce the negative effect of time-delay disturbances and enhance the performance of optimal set points. First, a data-driven time-delay disturbance observer (DTDO), based on fuzzy neural networks, is designed to describe the time-delay disturbances of optimal objectives. Then, the expressions and intervals of time-delay disturbances can be estimated to improve the accuracy of optimal objectives. Second, a hybrid evolutionary robust optimization (HERO) algorithm, which combines an expectation robust optimization strategy with a min–max evolutionary robust optimization strategy, is proposed to solve optimal set points. Then, the average robustness and conservative of robustness optimal set points can be improved. Third, an adaptive time-delay controller using Lyapunov–Krasovskii functionals is proposed to track optimal set points. Then, control accuracy is improved while ensuring control stability. Finally, the superior performance of HERO-OC is compared with some novel optimal control methods in a second-order nonlinear system and a wastewater treatment process.

Duality Method for Multidimensional Nonsmooth Constrained Linear Convex Stochastic Control

In this paper, we discuss a general multidimensional linear convex stochastic control problem with nondifferentiable objective function, control constraints, and random coefficients. We formulate an equivalent dual problem, prove the dual stochastic maximum principle and the relation of the optimal control, optimal state, and adjoint processes between primal and dual problems, and illustrate the usefulness of the dual approach with some examples.

The Evaluation Technology of Manufacturer Intelligence Regarding the Selection of the Decision Support System of Smart Manufacturing Technologies: Analysis of China–South Africa Relations

With the development of international cooperation, South Africa (SA) has been China’s largest trading partner in Africa for several consecutive years. China and SA can build the digital “Belt and Road” to modernize the manufacturing system locally and optimize process control by benchmarking with the best-in-class manufacturers in each country. In this research, an evaluation technology of manufacturer intelligence regarding the selection of decision support system (DSS) of smart manufacturing technologies, analyzing China–South Africa relations, is described. Firstly, the three keys aspects that enable the technologies of DSS are discussed in detail. Then, one key technology, the manufacturers’ intelligent evaluation system with 15 indexes, was built. The indexes and their measurements are also proposed. Finally, a fusion method based on boosting with multi-kernel function (online sequential extreme learning machine based on boosting, Boosting-OSELM) is introduced. The purpose of Boosting-OSKELM is to combine several weak learners into a strong learner (lower mean square error, MSE) through an acceptable time delay. Finally, the case study is presented to demonstrate the improvement on the MSE and process time, showing a relative MSE improvement of 96.19% and a relative time delay ratio of 31.46%. Totally, the largest contribution of the proposed evaluation method in this study is the conversion of the history data saved by the manual scoring method into knowledge in accessible MES and resealable time delay, which will free up the expert workforce in the entire process. We expect this paper will help future research in this field.

Intelligent system for analyzing battery charge consumption processes

The article develops an intelligent system of analysis and neural network forecasting of battery charge consumption for automated vehicles (AGVs). For this purpose, the types of AGV and the methods of effective forecasting of their battery charge consumption were analyzed. It is established that they are based on optimal robot control processes; application of technologies to increase capacity and extend service life. The data for the forecast was collected using the UAExpert OPC UA client, which allowed to convert the informative components of the data vector into a format suitable for further processing (csv). To eliminate outliers in the signals, a dispersion analysis of each parameter of AGV was carried out. Data for which the sigma value exceeded 1.5 were considered partialle lost and were replaced by a moving average of 12 points (the number of ANN inputs). For training, verification and testing of neural networks, parameters with high and medium positive correlation dependence were selected according to the Pearson correlation coefficient. Short-term and medium-term forecasting of battery charge consumption for AKTZ was carried out on the basis of ANN with deep learning, the model of which was tested in two modes: forecasting and prediction. The effectiveness of the developed system was investigated by testing it on the data obtained from Formica-1 AGV. The average absolute testing error was less than 1%. The highest value of the prediction error was less than 9 % when predicting such parameters as current position and X-coordinate, which are correlated with battery charge consumption for AGV. It has been established experimentally that the accuracy of the forecast of battery charge consumption for various types of AGV has been improved.

Optimal Control of Technological Processes

The paper formulates conditions under which the roots closest to the imaginary axis (critical roots) of the characteristic equation of a linearized system are real for the maximum possible degree of stability of the closed-loop control system of a technological process with pure delay. For the parameters of the controllers corresponding to the maximum degree of stability, these roots are multiples. Their multiplicity order is one more than the number of coefficients in the transfer function of the controller. It is demonstrated that for a typical technological control object, these conditions are satisfied for all “serial” control laws. This allowed for obtaining analytical expressions for optimal settings and limiting degrees of stability as functions of object parameters for typical dynamic characteristics of technological processes. The paper considers the problem of robust stability for control systems with an object containing pure delay. It has been proven that in the maximum stability problem, the operations of maximizing over controller parameters and minimizing over the set of possible object parameters can be interchanged. Therefore, selecting robust settings amounts to determining the minimum of the maximum stability over the set of possible object parameter values. Controllers with such settings are suitable, without modification, for a whole class of technological processes.

Application of the Mathematical Model of the Functional Breathing System for Optimal Control of the Training Process of Highly Qualified Athletes

Application of the Mathematical Model of the Functional Breathing System for Optimal Control of the Training Process of Highly Qualified Athletes

Optimal Control of Batch Crystallization Processes with Primary Nucleation

Multi-objective optimization problems for batch crystallization processes with primary nucleation are presented and solved using optimal control theory. The mass of the nucleated crystals is tracked and included in the material balance. The resulting two-point boundary-value problems are difficult to solve because the expressions for the derivatives of the states and costates are highly non-linear. Conventional shooting methods usually fail to converge. Therefore, a gradient-based algorithm is applied instead. The method is illustrated by computing Pareto fronts for multi-objective optimization problems. An inherent trade-off is observed between the competing objectives of minimizing the number of nucleated crystals and the nucleated mass or the weight mean size. The algorithm is found to be both fast and robust, suggesting that it might be suitable for online model-based control.

Reinforcement learning-based approach for optimizing solvent-switch processes

In chemical and pharmaceutical industries, process control optimization is a crucial step to improve economical efficiency and the environmental impact. The current state-of-practice heavily relies on expert knowledge and extensive lab experiments. This not only increases the development time but also limits the discovery of new strategies. In this study, we propose Reinforcement Learning-based optimization approach for solvent-switch processes. We utilize a digital twin as the environment for a process designed to switch the THF to 1-propanol. A reward function is created for minimizing the process time and constraints are implemented using logarithmic barrier functions. A PPO agent is trained on the environment. The agent proposed a novel strategy that combines two conventionally separate phases, evaporation and constant volume distillation. This strategy resulted in an overall cost decrease of 24.9% compared to the baseline strategy. Moreover, results were verified experimentally on a pilot plant of Johnson & Johnson (J&J).

Dynamic crystal monitoring method for crystallization process based on deep learning

The real-time monitoring of the crystallization process plays an important role in the optimal control of the crystallization process. Because of the characteristics of multiple concurrences and irregular morphology in the crystallization process, this paper starts from the two aspects of target detection and image segmentation to explore the actual and effective dynamic monitoring method in the crystallization process. By using YOLOV5s target detection algorithm to detect the crystal position in the image to determine the target position. In addition, the adaptive threshold segmentation method and Sobel edge feature extraction algorithm are used to achieve accurate positioning and segmentation of crystal target, to achieve the position detection of crystal in the image and accurate segmentation of edge information.

Assessment Efficiency Evaluation of Al-Diwaniya Sewage Treatment Plant in Iraq

This study aims to evaluate the performance of the sewage treatment plant in Al-Diwaniya, one of cities in the southern part in Iraq. This evaluation could be used to facilitate effluent quality assessment or optimal process control of the plant. The influent reaching the plant is considered a medium to strong in strength with BOD5/COD ratio in the range 0.23 and 0.69 which can be considered an easily degradable sewage by the biological processes performed by the activated sludge unit. The quality of the effluent was found to be higher than the Iraqi standards for disposal to water bodies. The BOD5/COD ratios of the treated sewage varied over a wide range as low of 0.13 to 1.48 indicating operational problems in the plant. Regression analysis was performed to estimate the removal percentages of BOD5, COD, TSS and NO3 that the plant should perform by to reach the disposal limitations.

Heavy metal remediation using chelator-enhanced washing of municipal solid waste compost based on spectroscopic characterization

Due to high metal toxicity, mixed municipal solid waste (MSW) compost is difficult to use. This study detected the presence of heavy metals (Cd, Cu, Pb, Ni, and Zn) in MSW compost through mineralogical analysis using X-ray diffraction (XRD) and performed topographical imaging and elemental mapping using a scanning electron microscope and energy dispersive X-ray analysis (SEM-EDX). Ethylenediaminetetraacetic acid (EDTA), a typical chelator, is tested to remove heavy metals from Indian MSW compost (New Delhi and Mumbai). It deals with two novel aspects, viz., (i) investigating the influence of EDTA-washing conditions, molarity, dosage, MSW compost-sample size, speed, and contact time, on their metal removal efficiencies, and (ii) maximizing the percentage removal of heavy metals by determining the optimal process control process parameters. These parameters were optimized in a batch reactor utilizing Taguchi orthogonal (L25) array. The optimization showed that the removal efficiencies were 96.71%, 47.37%, and 49.94% for Cd, Pb, and Zn in Delhi samples, whereas 45.55%, 79.52%, 59.63%, 82.31%, and 88.40% for Cd, Cu, Pb, Ni, and Zn in Mumbai samples. Results indicate that the removal efficiency of heavy metals was greatly influenced by EDTA-molarity. Fourier-transform infrared spectroscopy (FTIR) confirmed the presence of hydroxyl group, which aids heavy metal chelation. The results reveal the possibility of EDTA to reduce the hazardous properties of MSW compost.

Simulation-Based Optimization of Injection Molding Process Parameters for Minimizing Warpage by ANN and GA

Plastic injection molding is one of the most used methods for producing plastic products because it can be produced at a high production rate, low cost, and ease in manufacturing. However, one defect that affects product quality is namely warpage. To reduce plastic product warpage, the injection molding process is required optimal process control to increase plastic product quality. The objective of this paper is to optimize injection molding process parameters for minimizing the warpage of plastic glass. The optimization process is divided into two phases. The Finite Element Method (FEM) was employed in the first phase to simulate 32 experiments under various parameters. The parameters of this process consist of melt temperature ranging from 180 to 230 °C, mold temperature in the range of 20 – 45 °C, filling time from 0.82 to 0.92 s, packing time ranging from 5.88 to 7 s and cooling time of 14 to 18 s. In the second phase, Artificial Neural Network (ANN) combined Genetic Algorithm (GA) was developed to predict the warpage and solve the optimization process to find optimal parameters. Combining the intelligent method shows that ANN and GA effectively find the optimal process parameters that can reduce the warpage of the product by 35.73% from the maximum value.

Ecological index analysis of growth and development of Procambarus Clarkii based on biological characteristics

As the scale of Procambarus clarkii culture is gradually mature, it has become the focus of work to accurately detect the biological characteristics of each stage and improve the construction of ecological comprehensive indicators for the growth and development of Procambarus Clarkii. For this purpose, an ecological index evaluation model for growth and development of Procambarus clarkii based on biological characteristics was proposed. According to the evaluation data of ecological indicators of growth and development of crayfish, the data of ecological indicators of growth and development of crayfish were reconstructed with fuzzy correlation constraints, and the process optimization control was used to evaluate the ecological indicators of growth and development of crayfish. The results show that the change trend of the mean value of the ecological index obtained by this evaluation method is consistent with that of the standard value, and the mean value of the reconstructed index reaches the maximum value in the third detection interval, which is 0.85. In the fourth detection interval, the growth tended to be stable and the growth index decreased to 0.37. Each index element conforms to the growth law and the actual situation, so the ecological index evaluation system has high confidence, strong adaptability and accurate evaluation results, which provides a reference for establishing a more accurate and convenient evaluation system.

Economic optimization and predictive control for nonlinear systems using Lyapunov based nonlinear cost function design

Optimal control of physical processes depends on the formulation of objective functions and contemplation of appropriate predictive control. In this article, we propose a new framework, to blend the concepts of the Lyapunov-based economic model predictive control (Ly-EMPC) scheme with the novel cost-efficient profit function design. Furthermore, Lyapunov’s stability criteria is employed to verify the existence of duality and optimality for the designed economic stage costs. This method differs from the existing EMPC techniques by implementing Lagrange multipliers in the rotated cost function as a measure of stability. The closed-loop performance using the regularized Ly-EMPC is validated against a standard tracking objective-based nonlinear MPC for three benchmark examples through an econometrics gauge factor. Interestingly, in contrast to tracking NMPC, the proposed Ly-EMPC scheme excels in economic performance, while the formation of nonlinear economic stage cost is conceptually simpler, novel, and practically profitable.

Dynamic modeling of multi-input and multi-output controlled object for municipal solid waste incineration process

Municipal solid waste incineration (MSWI) is an important part of energy recovery and sustainable development, constructing the model of controlled object is the basis of studying its optimal control. MSWI is a typical multi-input and multi-output industrial process, which has many uncertain characteristics such as strong nonlinearity and multivariable coupling. To solve this problem, a model of controlled object in MSWI process is proposed in this paper. Firstly, the MSWI process based on grate incinerator is analyzed to summarize its relevant variables and control requirements. Secondly, the solid phase and gas phase of MSWI process are divided to construct material balance equations of subsystems. Next, the energy transfer process in incinerator is deduced and calculated through thermodynamic laws. Finally, the validity of the model is verified by the process data of a MSWI plant in Beijing, China, which lays a model foundation for the study of the optimal control of MSWI process.

Morphology, Histology, and Histochemistry of the Digestive Tract of the Marbled Flounder Pseudopleuronectes yokohamae

This study investigated the morphological, histological, and histochemical characteristics of the digestive tract of the marbled flounder (Pseudopleuronectes yokohamae). The relative length of the gut of the marbled flounder digestive tract was 1.54 ± 0.10 (n = 20), and it had a simple stomach and 6-9 pyloric caeca. The mucosal folds of the marbled flounder digestive tract exhibited a general branched morphology. The thickness and mucosal fold length of the intestinal muscularis externa showed similar aspects in all areas. The thickness of the intestinal muscularis externa was the thickest in the posterior intestine portion, and the length of mucosal folds was the longest in the anterior intestine portion. It was indicated that food digested by gastric acid in the stomach moves to the anterior portion (including pyloric caeca) and mid portion of the intestine, ensuring effective stimulation of cholecystokinin (CCK)-producing cells. In addition, the distribution pattern of CCK-producing cells in the intestine was very similar to that of mucus-secreting goblet cells. The CCK-producing cells and goblet cells in the marbled flounder were well-adapted to promote optimal control of the digestive process. Based on the morphological and histochemical studies, it was concluded that the marbled flounder displays a digestive tract comparable to that of fish species with carnivorous habits.

Optimal Scheduling of Combined Electric and Heating Considering the Control Process of CHP Unit and Electric Boiler

In order to solve the problem of new energy consumption, a combined electric and heating system (CEHS) dynamic optimal scheduling method considering the optimal control of combined heat and power (CHP) unit and electric boiler is proposed from the perspective of unit technology transformation, to optimize the thermoelectric coupling relationship and improve the regulation capacity of the CEHS. Firstly, the electric and heat output models of CHP units considering the optimal control process, were constructed and used to analyze the electric–thermal characteristics and the impact of unit pressure safety under variable load input. On this basis, CHP units, electric boilers, wind power units, and thermal power units are optimally scheduled to minimize system operating costs. Finally, a simultaneous method of “discrete first, optimize later” is proposed to solve the dynamic optimal scheduling problem. The simulation results verify that the optimal scheduling considering the optimal control of CHP units and the retrofitting of electric boilers can promote the consumption of wind power and improve the overall operating economy of the system while ensuring the feasibility of the CEHS scheduling scheme.

Control and optimization of abstract continuous time evolution inclusions

Abstract Abstract controlled evolution inclusions are revisited in the Banach spaces setting. The existence of solution is established for each selected control. Then, the input–output (or, control-states) multimap is examined and the Lipschitz continuous well posedness is derived. The optimal control of such inclusions handled in terms of a Bolza problem is investigated by means of the so-called P ℱ format of optimization. A strong duality is provided, the existence of an optimal pair is given and the system of optimalty is derived. A Fenchel duality is built and applied to optimal control of convex process of evolution. Finally, it will be shown how the general theory we provided can be applied to a wide class of controled integrodifferental inclusions.

Applications MLP and Other Methods in Artificial Intelligence of Fruit and Vegetable in Convective and Spray Drying

The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes in the methods used to examine and study food and its microbiological aspects. It should be added that a new trend of bioactive ingredient consumption is also on the increase, which translates into numerous attempts that are made to keep the high quality of those products for a longer time. New and modern methods are being sought in this area, where the main aim is to support drying processes and quality control during food processing. This review provides deep insight into the application of artificial intelligence (AI) using a multi-layer perceptron network (MLPN) and other machine learning algorithms to evaluate the effective prediction and classification of the obtained vegetables and fruits during convection as well as spray drying. AI in food drying, especially for entrepreneurs and researchers, can be a huge chance to speed up development, lower production costs, effective quality control and higher production efficiency. Current scientific findings confirm that the selection of appropriate parameters, among others, such as color, shape, texture, sound, initial volume, drying time, air temperature, airflow velocity, area difference, moisture content and final thickness, have an influence on the yield as well as the quality of the obtained dried vegetables and fruits. Moreover, scientific discoveries prove that the technology of drying fruits and vegetables supported by artificial intelligence offers an alternative in process optimization and quality control and, even in an indirect way, can prolong the freshness of food rich in various nutrients. In the future, the main challenge will be the application of artificial intelligence in most production lines in real time in order to control the parameters of the process or control the quality of raw materials obtained in the process of drying.

Design and control of extraction combined with extractive distillation for separation isopropanol-butanol-ethanol-water with organic Rankine cycle

The high-purity isopropanol butanol ethanol (IBE) mixture is a highly efficient gasoline additive that can be directly used without further separation. The product of isopropanol-butanol-ethanol fermentation contains a large amount of water, and a large amount of energy will be consumed in the purification process. In this paper, the separation IBE process is systematically studied from the aspects of process optimization, energy recovery and process control. The operation parameters of extraction combined with extractive distillation (E-ED) are optimized by a multi-objective genetic algorithm. Then, the pinch point technique is used to analyze the energy usage of the optimized process, and the maximum heat recovery heat integration scheme is obtained. Compared with the optimized process, the low pressure steam consumption of the heat integration process is reduced by 100%, the cooling water consumption is saved by 58.80%, and the TAC is reduced by 35.95%. In view of massive waste heat in the heat integration of the extraction combined with extractive distillation (E-ED-HI) process, a new configuration combining the E-ED-HI and organic Rankine cycle (E-ED-HI-ORC) is further proposed. The organic Rankine cycle using R600a can further reduce TAC by 8.88% based on the HI-E-ED process. Finally, the control scheme of E-ED process is proposed. The performance of the control scheme is examined with ±10% disturbance of the feed flow rate and composition change, and the results show that the process of extractive distillation and separation of IBE has good controllability.