Process Control Strategies Research Articles

Environmental Analysis, Monitoring, and Process Control Strategy for Reduction of Greenhouse Gaseous Emissions in Thermochemical Reactions

This review paper illustrates the recommended monitoring technologies for the detection of various greenhouse gaseous emissions for solid waste thermochemical reactions, including incineration, pyrolysis, and gasification. The illustrated gas analyzers are based on the absorption principle, which continuously measures the physicochemical properties of gaseous mixtures, including oxygen, carbon dioxide, carbon monoxide, hydrogen, and methane, during thermochemical reactions. This paper illustrates the recommended gas analyzers and process control tools for different thermochemical reactions and aims to recommend equipment to increase the sensitivity, linearity, and dynamics of various thermochemical reactions. The equipment achieves new levels of on-location, real-time analytical capability using FTIR analysis. The environmental assessment study includes inventory analysis, impact analysis, and sensitivity analysis to compare the mentioned solid waste chemical recycling methods in terms of greenhouse gaseous emissions, thermal efficiency, electrical efficiency, and sensitivity analysis. The environmental impact assessment compares each technology in terms of greenhouse gaseous emissions, including CO2, NOx, NH3, N2O, CO, CH4, heat, and electricity generation. The conducted environmental assessment compares the mentioned technologies through 15 different emission-related impact categories, including climate change impact, ecosystem quality, and resource depletion. The continuously monitored process streams assure the online monitoring of gaseous products of thermochemical processes that enhance the quality of the end products and reduce undesired products, such as tar and char. This state-of-the-art monitoring and process control framework provides recommended analytical equipment and monitoring tools for different thermochemical reactions to optimize process parameters and reduce greenhouse gaseous emissions and undesired products.

Experimental study on rapid cold start-up performance of PEMFC system

The cold start-up of a proton exchange membrane fuel cell is considered one of the main factors affecting the commercialization of fuel cell vehicles. In this study, an automotive fuel cell system was designed and tested for cold start-up at low temperatures. In the absence of PTC (Positive Temperature Coefficient) heating device, the stack was directly loaded to generate heat, which provided the cold start-up characteristics of system at low temperatures. Cold start-up process and purging control strategies were analyzed at −20 °C and −30 °C. It was found that the fuel cell system could produce 50% power in 25 s at −20 °C, the coolant temperature's heating rate was 0.78 °C/s, the coolant outlet temperature could reach 20 °C within 40 s and no apparent low voltage of single cell occurred. While, the cell close to the end plate had low cell voltage and reverse polar phenomena throughout the −30 °C cold start-up process. The heating rate of the coolant temperature was 0.44 °C/s, and the temperature of coolant outlet reached 20 °C within 90 s. The purging time ranged from 180 to 260 s according to the voltage drop value of stack and the ohmic resistance of stack was 360–470 mΩ after the high-volume air purging at different tests. After 30 cold start-up tests, the rated point performance of the stack declined by about 1%, and the consistency of cell voltages did not change significantly. Future work will focus on optimizing cold start-up strategy and speeding up purging time to minimize the performance impact of the cold start-up.

Successive challenge for multi-batch overall pharmaceutical manufacturing control from two-step digital process to OQC strategy by integrated intelligent algorithm

The overall quality consistency (OQC) evaluation of Chinese meteria medica (CMM) from multi-batch is a paramount issue in digital production process. To solve this problem, an industrial information integrated strategy composed with a two-step digital process control and OQC diagnosis strategy are proposed in the complex system of big honeyed pill. The two-step digital control is successively invested to acquire the critical quality attributes (CQAs) so as to excavate the primary parameters to control the consistency from honey refining procedure to He-Tuo procedure especially through online near infrared (NIR) monitoring with physical and chemical attributes. The OQC diagnosis strategy is proposed to figure out multi-batch of OQC from different perspectives by integrating individual method through data fusion approach. As a result, three out of 30 batches of samples are discovered by the information integrated strategy with the weights of 27.82%, 44.60% and 27.58% for NIR, mid-infrared (MIR) and texture technologies. Moreover, the diagnosed samples are executed by the zebrafish efficacy as an in vivo test, which indicates that the OQC diagnosis strategy is a promising method. Besides, the proposed information integrated strategy provides theoretical and practical cornerstones for guaranteeing the multi-batch overall quality especially for the digital and intelligent of CMM industrialization.

Advanced model predictive control strategies for evaporation processes in the pharmaceutical industries

In this paper we present a framework to design control systems for an evaporation process in the pharmaceutical industry with the aim to deliver guaranteed operability for different molecules and under different thermodynamic scenarios. Based on a mathematical model developed within the gPROMS platform calibrated and validated with real data from experiments, three control methods are implemented and compared, (i) Proportional Integrative Derivative control (PID), (ii) Model Predictive Control (MPC) and (iii) explicit/multi-parametric Model Predictive Control (mp-MPC). The performance and limits of the derived control schemes are then established and tested for reference tracking as well as disturbances rejection.

Physics-informed machine learning for MPC: Application to a batch crystallization process

This work presents a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model of the aspirin crystallization process is first developed to describe the formation of crystals through nucleation and growth. Then, the PIRNN modeling scheme is introduced to integrate observational data and mechanistic models for the development of machine learning models. Additionally, the physical constraints on process states are embedded in the machine learning models to prevent physically unreasonable predictions. Subsequently, the PIRNN model that captures the dynamic behavior of the batch crystallization process is utilized in the design of model predictive controller that optimizes the operation of the crystallizer. Through open-loop and closed-loop simulations, it is demonstrated that the PIRNN models using less training data achieve prediction accuracy and closed-loop performance comparable to the purely data-driven model.

A stage-wise non-Gaussian statistical process control strategy

The article delves into the development of a Non-Gaussian Process Monitoring Strategy for a Copper Cathode Manufacturing Unit (CCMU). The monitoring strategy being devised highlighted the issue of multi-stage process monitoring via the usage of Multi-block Independent Component Analysis (MBICA) techniques. MBICA is the multi-block variant of ICA technique which is prevalently used for process laden with non-Gaussian or non-normal data. Development of the monitoring strategy involved detection of fault(s) and their subsequent diagnosis. Detection of fault(s) was carried out by employment of I2 control chart whose control limit was established via Bootstrap procedure. The diagnosis of the detected fault was carried out by employment of fault diagnostic statistic. An amalgamation of MBICA and Multivariate Exponentially Weighted Moving Average (MEWMA) are also known as MBICA-MEWMA approach was also proposed for detection of incipient fault(s). The monitoring strategy thus developed was showcased for a CCMU which specialised in the manufacture of copper cathode which has got varied practical applications. The monitoring strategy thus devised was able to detect and diagnose the faults with appreciable accuracy.

Process control: simply a matter of efficiency or of survival and costs? A single-centre quality improvement project in living donor renal transplant

BackgroundRising incidence and prevalence of end-stage renal disease is a worldwide concern for sustainability of healthcare systems and societies. Living donor renal transplant [LDRT] provides highest health achievements and cost containment than any alternative form of renal replacement therapy. Nonetheless, about 25% of potential LDRTs are missed for causes directly related with inadequate timing in donor assessment. Our quality improvement (QI) project implement process control tools and strategy aiming at reducing total evaluation time for donor candidates and minimizing dialysis exposure for intended recipients, which are the two main determinants of clinical outcomes and costs.MethodsThe study includes patients who underwent donor nephrectomy between January 1, 2017 and December 31, 2021. Six Sigma DMAIC approach was adopted to assess Base Case performance (Jan2017-Jun2019) and to design and implement our QI project. Study of current state analysis focused on distribution of time intervals within the assessment process, analysis of roles and impacts of involved healthcare providers and identification of targets of improvement. Improved Scenario (Jul2019-Dec2021) was assessed in terms of total lead time reduction, total pre-transplantation dialysis exposure and costs reduction, and increase in pre-emptive transplantations. The study was reported following SQUIRE 2.0 Guidelines for QI projects.ResultsStudy population includes 63 patients, 37 in Base Case and 26 in Improved Scenario. Total lead time reduced from a median of 293 to 166 days and this in turn reduced pre-transplantation dialysis exposure and costs by 45%. Rate of potential pre-emptive donors’ loss changes from 44% to 27%.ConclusionsLean methodology is an effective tool to improve quality and efficiency of healthcare processes, in the interest of patients, healthcare professionals and payers.

Advanced Process Control Strategy for a Condensate Stabilization Unit: Energy, Exergy, Economic, and Environmental (4E) Study

This study assesses the energy, exergy, economic, and environmental performance of a gas stabilization unit by employing a new strategy to implement an advanced control system. The advanced process control (APC) strategy is established based on the response surface methodology to assess the real-time performance of the process. The RSM-based APC technique targets the energy, exergy, economic, and environmental (4E) performance of the process in unsteady-state operations. A detailed sensitivity analysis is also conducted to evaluate the relative significance of operating parameters on the objective functions, such as exergy efficiency, energy cost, and CO2 emission. The RSM-based APC strategy proposes an algorithm that can readily be implemented in the plant to increase the plant’s energy performance. Implementing this intelligent control system leads to the production of standard products, while production cost, energy consumption, exergy efficiency, and environmental impacts are enhanced. The exergy and sensitivity results indicate that the stabilization tower has the highest potential for process enhancement, and reboiler temperature is the most influential factor among operating parameters. Also, the RSM-based APC improves the exergy efficiency of the plant by 19% and reduces the energy cost by 13%. At the optimal state, 457 kg/h CO2 emission is prevented.

Novel shift process control strategy for two-speed clutchless automatic mechanical transmission based on segmented speed regulation

Battery electric vehicles (BEV) equipped with two-speed automatic mechanical transmission (AMT) have gained increasing popularity owing to advantages of a widened high-efficiency operation zone and system costs. In this study, a combined gear shift control strategy including a segmented speed regulation control and torque recovery control was developed for a two-speed AMT in BEV. The switching point of proportional integral control and sliding mode control during speed regulation process is adaptively optimized to ensure minimization of the shift time, while the maximum vehicle jerk is controlled within a defined range. The effectiveness of proposed control strategy is verified by a test bench. Results show that proposed control strategy can not only ensure the impact degree fluctuates within a predetermined range, but also achieve fast and accurate speed regulation which can reduce the speed regulation time by 17%–33%.

In situ characterization of material extrusion printing by near-infrared spectroscopy

Material extrusion printing of reactive resins and inks present a unique challenge due to the time-dependent nature of the rheological and chemical properties they possess. As a result, careful print optimization or process control is important to obtain consistent, high quality prints via additive manufacturing. We present the design and use of a near-infrared (NIR) flow through cell for in situ chemical monitoring of reactive resins during printing. Differences between in situ and off-line benchtop measurements are presented and highlight the need for in-line monitoring capability. Additionally, in-line extrusion force monitoring and off-line post inspection using machine vision is demonstrated. By combining NIR and extrusion force monitoring, it is possible to follow cure reaction kinetics and viscosity changes during printing. When combined with machine vision, the ability to automatically identify and quantify print artifacts can be incorporated on the printing line to enable real-time, artificial intelligence-assisted quality control of both process and product. Together, these techniques form the building blocks of an optimized closed-loop process control strategy when complex reactive inks must be used to produce printed hardware.

Learning curve and analysis of curative effects after balloon pulmonary angioplasty for chronic thromboembolic pulmonary hypertension.

Learning curve and analysis of curative effects after balloon pulmonary angioplasty for chronic thromboembolic pulmonary hypertension.

Machine Learning-Based MPC of Batch Crystallization Process Using Physics-Informed RNNs

This work presents a framework for developing physics-informed recurrent neural network (PIRNN) models and PIRNN-based predictive control schemes for batch crystallization processes. The population balance model of aspirin crystallization process is first developed to describe the formation of crystals through nucleation and growth. Then, the PIRNN modeling scheme is introduced to integrate observational data and mechanistic models for the development of machine learning models. Subsequently, the PIRNN model that captures the dynamic behavior of the batch crystallization process is utilized in the design of model predictive control. Through open-loop and closed-loop simulations, it is demonstrated that the PIRNN models using less training data achieve prediction accuracy and closed-loop performance comparable to the purely data-driven model.

Design of robust fuzzy iterative learning control for nonlinear batch processes.

In this paper, a two-dimensional (2D) composite fuzzy iterative learning control (ILC) scheme for nonlinear batch processes is proposed. By employing the local-sector nonlinearity method, the nonlinear batch process is represented by a 2D uncertain T-S fuzzy model with non-repetitive disturbances. Then, the feedback control is integrated with the ILC scheme to be investigated under the constructed model. Sufficient conditions for robust asymptotic stability and 2D $ H_\infty $ performance requirements of the resulting closed-loop fuzzy system are established based on Lyapunov functions and some matrix transformation techniques. Furthermore, the corresponding controller gains can be derived from a set of linear matrix inequalities (LMIs). Finally, simulations on the three-tank system and the highly nonlinear continuous stirred tank reactor (CSTR) are carried out to prove the feasibility and efficiency of the proposed approach.

Experimental Study on Flow Characteristics of Large-scale Liquid Oxygen Delivery System

To deeply explore the flow law of the large-scale liquid oxygen delivery system, several liquid oxygen delivery tests have been conducted and measured. On this basis, the model of the pipeline characteristic of the liquid oxygen delivery system is constructed according to the hydrodynamic theory to obtain the relationship equation between the gas cushion pressure and liquid oxygen flow, and the frequency conversion model of the liquid oxygen pump is constructed according to the pump affinity laws to obtain the relationship equation between the liquid oxygen pump frequency, flow, and head. Under the working pressure limitation of the storage tank, liquid oxygen pump, and pipeline, the ultimate capacity of the liquid oxygen delivery system is calculated using the above models. This provides a database and theoretical basis for further optimization of liquid oxygen delivery process parameters and control strategies, as well as evaluation and exploitation of system performance potentials.

Mathematical model and dynamic simulink simulation of PEM electrolyzer system

Hydrogen, being the most abundant element in the universe, holds great promise as an energy carrier for decarbonizing various economic sectors. In particular, green hydrogen production through water electrolysis is essential for achieving this goal, with polymer electrolyte membrane (PEM) water electrolyzers playing a crucial role. PEM water electrolyzers are known for their rapid response, enabling them to effectively adapt to fluctuations in renewable energy sources. Nevertheless, rapid load changes can result in the rapid build-up of heat within the electrolytic cell, leading to a sharp increase in temperature and potentially harming the cell. To address this challenge, we developed an electrolysis water system model using MATLAB and validated its accuracy through experiments. This model allowed us to explore the factors influencing stack temperature and propose a fast and secure dynamic process control strategy. By laying the groundwork for subsequent control studies on PEMEC (Proton Exchange Membrane Electrolysis Cell) stacks and systems, this research facilitates further progress in their control and regulation.

Models of environmental safety monitoring in the management of production processes in quarries

Currently, the data obtained during the operation of mining enterprises are not sufficiently used to assess the pollution of the natural environment. In this regard, the purpose of this work is to improve the environmental safety of the functioning of mining enterprises by developing structural and functional schemes for the modernization of technological work management processes and integrating tools for assessing the dynamics of environmental conditions based on hybrid simulation methods and Industry 4.0 technologies. Within the framework of the work done, a structural and functional model of modernization of the technological process control scheme of open-type mining enterprises for dynamic assessment of the environmental condition of polluted territories was proposed, and a software implementation of the application was also implemented, which allows integrating the obtained results into the current information environment of mining enterprises.

Інтелектуальна система дозування коагулянтів для очищення води на основі штучної нейронної мережі

The article reveals the essence and features of an artificial neural network, which is used to regulate water purification processes. Features, principles and main stages of water purification are determined. The stages of learning artificial neural networks are disclosed. The approach to the use of artificial neural networks during dosing of the mixture for water purification is substantiated. The process of dosing the mixture for water purification and the related indicators, which are important for the implementation of the water purification process, are analyzed. A number of factors that directly affect the coagulation process and, as a result, the structure of the neural network include turbidity and flow speed. It is shown that determining the dose of coagulant is necessary to minimize time, implement a continuous process, stabilize variations in the operator’s observations, and improve the quality of water treatment. A coagulant dose adjustment mode is proposed, as well as a water purification process control scheme based on the developed artificial neural network with unsupervised learning, which is used to optimize the coagulant dosage in the water purification process.

A multiple neural network and reinforcement learning-based strategy for process control

In this study, we propose a novel process control strategy by assimilating multiple neural network (MNN) and reinforcement learning (RL) to generate an MNNRL controller. The artificial neural networks (ANNs) constituting the MNN are trained using repositories of optimal control and state data to predict control actions to attain or stay at a set-point. This ability is significantly improved by developing an RL component for the MNNRL controller to provide enhanced set-point tracking, robustness against disturbances, and real-time learning to successfully adapt in the presence of persistent parameter upsets. The controller performance is examined in three different case studies utilizing the simulation of continuous processes with varying degrees of nonlinearity. Relative to both MNN and nonlinear model predictive control (NMPC), the MNNRL controller is found to provide better performance with about 32% less lower integral absolute error (IAE), and 50% reduced settling time with suppressed over-/under-shoots in controlled variables. Further, it is observed that the controller is fairly robust against disturbances, and can adapt via real-time learning to altered process dynamics caused by any persistent process upsets. The controller computation time is observed to be about an order of magnitude less in comparison to NMPC.

Image-Based Feedback Control Using Tensor Analysis

In manufacturing systems, many quality measurements are in the form of images, including overlay measurements in the semiconductor manufacturing and dimensional deformation profiles of fuselages in an aircraft assembly process. To reduce the process variability and ensure on-target quality, process control strategies should be deployed, in which the high-dimensional image output is controlled by one or more input variables. To design an effective control strategy, the process model should be first estimated via relationship exploration between the image output and inputs, off-line. Next, the control law is formulated by minimizing the control objective function online. The main challenges of achieving such a control strategy include (i) the high dimensional output of a regression model, (ii) the integrated analysis of both the spatial structure of image outputs and the temporal structure of the image sequence, and (iii) non-iid noises. To address these challenges, we propose a novel tensor-based process control approach by incorporating the tensor time series and regression techniques. Based on the process model, we can then obtain the control law by minimizing a control objective function. Although our proposed approach is motivated by a 2D image case, it can be extended to higher-order tensors such as point clouds. Simulation and case studies show that our proposed method is more effective than benchmarks in terms of relative mean square error.

Assessing residence time distributions and hold-up mass in continuous powder blending using discrete element method

Continuous manufacturing of pharmaceutical products can improve process efficiency and quality assurance. The propagation of material through continuous manufacturing processes can be characterized by residence time distributions (RTDs). Conventional parameter-fitted RTD models rely extensively on experimental measurements that are often expensive and limited to available observation methods and instruments. This limitation can be compensated by discrete element method (DEM) simulations. This work evaluates the predictive capabilities of DEM modeling for a continuous powder mixing process. First, the developed DEM model was validated using experimentally measured RTDs. Then, the effects of the mass flow rate, blender element pattern, and blender speed on mean residence time and hold-up mass were studied. The simulation results showed that the mean residence time increased linearly with increasing number of mixing elements but decreased with increasing mass flow rate and blender speed. The hold-up mass increased with increasing mass flow rate and number of mixing elements but decreased with increasing blender speed. The work successfully demonstrated the capability of the DEM model in improving our understanding regarding the impacts of the blender operating conditions on the RTD and hold-up mass as well as in supporting the assessment of control strategies for continuous blending processes.