Process Control Performance Research Articles

A more general incremental inter-agent learning adaptive control for multiple identical processes in mass production

Mass production has been a trend for modern manufacturing. Information from peer processes may be used to improve the individual process control performance. The idea of ‘incremental step mimicking’ has been presented in our previous work [1] which resulted in ‘incremental inter-agent learning’ (IIAL) adaptive control. However, that work is based on a primitive adaptive control in which the control input is directly calculated from the online-identified model. This paper explores the inter-agent learning adaptive control based on a more complicated adaptive control, and proposes a more general Full-Scale IIAL (FS-IIAL) of which the previous IIAL [1] can be viewed as a special case. With the ensured robust stability, the proposed work is applied on the case when LIP formulae is a single layer RBF neural network, and simulation result validates the superior control performance of each process over the original adaptive control and the previous IIAL.

An Optimal Fuzzy Control Method for Nonlinear Time-Delayed Batch Processes

An optimal fuzzy controller design scheme is proposed to address the influence of time delay and disturbance on the control performance of nonlinear batch processes. First, a two-dimensional (2D) equivalent Takagi-Suguno (T-S) fuzzy error model is formulated. By introducing a quadratic performance index function and adopting 2D Lyapunov-Krasovskii theory, the existence condition of the optimal fuzzy control law is given. Furthermore, its solvable condition, which depends on time-delay bounds, is constructed in terms of linear matrix inequalities, and its gain is obtained by using an optimization algorithm. This design has the advantages of faster tracking and better tracking performance. Finally, two different algorithms (with and without optimization) are used to control the water level of a triple-capacity water tank. The results show that the presented strategy is more effective and feasible.

Determination of Moisture Sorption Isotherms of Rice and Husk Flour Composites

HighlightsMoisture sorption isotherms of rice and husk flour composites were determined.Adsorption isotherms were best modeled by the Chung-Pfost and Oswin equations.Desorption isotherms were best modeled by the Polynomial and Chung-Pfost equations.The Modified Oswin model was the best for both adsorption and desorption.Abstract. Empirical models describing isotherms specifically for adsorption and desorption have not been described for rough rice and husk flour composites. Such models are vital for process control and monitoring operations which use rice husk and rice flours or their mixtures for food or material processing. The current study was undertaken to determine the moisture sorption isotherms of rice husk flour, rough rice flour and their mixtures subjected to different temperature levels. Effects of rice husk flour to rough rice flour ratio of 0:1 (0.0%), 1:49 (2.0%), 1:16 (5.9%), and 1:0 g/g (100.0%) on rough rice moisture isotherms at temperature levels of 20°C, 40°C, and 60°C were investigated. The dynamic dewpoint isotherm technique (DDI) was used to generate accurate isotherms. Several empirical models were tested to fit the experimental EMC data. All the isotherms showed typical sigmoidal type 2 shapes. The equilibrium moisture content (EMC) over equilibrium relative humidity (water activity) ranging from 10% to 95% showed temperature dependence. Hysteresis was evident for all samples, with a decreasing level at a higher temperature. Rice husk flour to rough rice flour ratio, as a factor, showed a significant effect on the EMC of rough rice. The EMC decreased with an increase in rice husk flour levels. Chung-Pfost and Oswin were the two best models for describing adsorption isotherm, and Polynomial and Chung-Pfost were the best models for fitting the desorption isotherm. Modified Oswin model was the best model amongst the two-variable models for describing both adsorption and desorption isotherms. Keywords: Empirical models, Equilibrium moisture content, Rice husk flour, Rough rice flour, Sorption isotherms.

Prediction of Microstructure Evolution for Additive Manufacturing of Ti-6Al-4V

A key challenge for successful exploitation of additive manufacturing (AM) across a broad range of industries is the development of fundamental understanding of the relationships between process control and mechanical performance of manufactured components. The present work is focused on the development of predictive methods for process-structure-property control of AM. In particular, laser beam powder bed fusion (PBF-LB) is identified as a key process for manufacture of metallic AM components. Ti-6Al-4V alloy is an important metal alloy for numerous high-performance applications, including the biomedical and aerospace industries. This paper presents initial developments on a model for microstructure prediction in PBF-LB manufacturing of Ti-6Al-4V, primarily focused on solid-state phase transformation and dislocation density evolution. The motivation is to quantify microstructure variables which control mechanical behavior, including tensile strength and ductility. A finite element (FE) based model of the process is adopted for thermal history prediction. Phase transformation kinetics for transient non-isothermal conditions are adopted and implemented within a stand-alone code, based on the FE-predicted thermal histories of sample material points. The evolution and spatial variations of phase fractions, α lath width and dislocation density are presented, to provide an assessment of the resulting microstructure-sensitivity of mechanical properties.

Investigating the identification of atypical sugarcane using NIR analysis of online mill data

In any given season thousands of tonnes of sugarcane with atypically low quality can pass undocumented through Australian sugarcane mills. Sugarcane with atypically low quality can negatively impact mill processes and throw off grower payment calculations. Mill laboratory operators often observe a small subset (1–5%) of cane consignments that have an unusually low juice Pol (Pij; a measure of sucrose content) relative to juice brix (Bij; a measure of dissolved sugars), that can indicate deteriorated or contaminated cane. Many mills only test a small subset of cane in the laboratory, with the majority of consignments analysed using fast near infrared (NIR) spectroscopic techniques. This means the true extent of ‘atypical’ consignments cannot be identified. To address this limitation, this paper compares five modelling approaches: Linear discriminant analysis (LDA), partial least squares discriminant analysis (PLS-DA), random forest (RF), Artificial Neural Networks (ANN) and Support Vector Machines (SVM). Model performance was reported as the correct classification rate (CCR) of typical and atypical samples based on independent test sets. The best performance was achieved by PLS-DA (CCRatypical = 88.65% and CCRtypical = 88.75%), while ANN had the lowest performance (CCRatypical = 85.27% and CCRtypical = 83.66%). The methodology used in this paper could be used to identify atypical consignments allowing mills to track occurrences to farms and if necessary develop process control operations for atypical cane. Furthermore, the use of a relatively simple modelling technique such as PLS-DA means model updates can be made efficiently and with confidence as PLS is already well established within the industry.

Solvent-based post-combustion CO2 capture for power plants: A critical review and perspective on dynamic modelling, system identification, process control and flexible operation

Solvent-based post-combustion CO2 capture (PCC) appears to be the most effective choice to overcome the CO2 emission issue of fossil fuel fired power plants. To make the PCC better suited for power plants, growing interest has been directed to the flexible operation of PCC in the past ten years. The flexible operation requires the PCC system to adapt to the strong flue gas flow rate change and to adjust the carbon capture level rapidly in wide operating range. In-depth study of the dynamic characteristics of the PCC process and developing a suitable control approach are the keys to meet this challenge. This paper provides a critical review for the dynamic research of the solvent–based PCC process including first-principle modelling, data-driven system/process identification and the control design studies, with their main features being listed and discussed. The existent studies have been classified according to the approaches used and their advantages and limitations have been summarized. Potential future research opportunities for the flexible operation of solvent-based PCC are also given in this review.

Modern ICT to support the Operator controlling Complex Systems

Many process control and service operations in productions are carried out in control centers via visual display unit workstations. The operator's tasks often have great relevance for system safety.Modern Information and Communication Technologies (ICT) should support the operator's workflow and their supervisory role. Multimodal output devices with task adequate structures of relevant information, process variables and units intent to improve the situation awareness of the operating staff. Intuitively designed input devices should promote quick responses in time critical situations to prevent loss of control. Mobil Display Screen Equipment can help to improve the communication between operators in control centers and plant operators in the field.In order to support operators optimally, modern ICT should be selected, designed and implemented carefully in the operating concept. Additional it should be adaptable to the operator's needs in a wide variety of situations.Possible risks, requirements and measurements are described using examples of operation errors and better masteries of situations as improvements due to new ICT.Within Risk Assessments of the control system the requirements of ICT are determined to optimize the operators' workplace and workload in order to support the operators' safety behaviour for their own and other employees' occupational safety and health.

Wedge design for high-temperature ultrasonic flow rate measurement

Measuring flow of liquids in pipes is crucial for process control operations, including custody exchange, chemical manufacture and manufacture of foodstuffs. However, many of these processes are at elevated temperatures (up to 350 °C) where current commercially available ultrasonic systems are unable to operate or require shut-down of plant for flow-meter installation, such as with in-line, or wet, systems.Clamp-on ultrasonic flow meters are able to achieve flow measurements at high-temperatures, often with complex or heavy waveguides and stand-offs to keep the Pb(Zr,Ti)O3 (PZT) element away from the hot, process end, however with the introduction of commercially available high-temperature piezoelectric ceramics, this is starting to change.The final major design consideration is the ultrasonic wedge, which traditionally made from polymers or slower speed of sound materials, is not suitable for use above ∼200 °C. Presented here is a metallic wedge design, which offers a shear mode velocity closer to the longitudinal sound velocity of polymers but with exceptional temperature resistance offering a permanently installed flow monitoring solution for demanding industrial environments.A 1 MHz transducer with integrated 40-degree incident angle wedge in stainless steel exhibits a signal to noise of 29.3 dB in a Z mode path configuration, at temperatures up to 380 °C. This was shown to give a repeatable and stable flow rate measurement when connected to a commercial flow converter, on to a pipe containing mineral oil flowing at approximately 14.4 m3/hr.

Development of «whole» evaluation algorithm of the control quality of «cupola – mixer» melting duplex process

The object of research is the control of the «cupola – mixer» duplex process, the subject of research is the quality of the «whole» control system for melting. The term «whole» means that it is not individual control operations that are evaluated, but the ability of the control system operating in the workshop to provide specified requirements for the quality of cast iron.One of the most problematic places is the lack of algorithms for the managerial staff to obtain objective information about the quality of management of duplex melting processes. This leads to the fact that it is not possible to get an answer to the question in which particular area or at which particular time point of the process implementation violations were made regarding the control of the heat.In the course of the study, methods were used to calculate sample statistical functions, test hypotheses about the form of the distribution law, regression analysis, analysis of variance, and probability theory methods. These methods were integrated into a general algorithm, called the algorithm for assessing the «whole» quality of control of the duplex melting process.The proposed algorithm allows to quantify the quality of duplex process control by such an indicator as the probability of deviations in chemical composition. As well as the duration of the unsteady mode corresponding to the stabilization time of the chemical composition by process control operations provided for by the control system adopted in the workshop. The practical implementation of the algorithm based on a series of industrial meltings has been completed.The obtained results of testing the algorithm make it possible to consider it an effective tool for obtaining objective information about the quality of control of the duplex process of «cupola – mixer» melting. This is due to the fact that the correct formulation and solution of the problem ensured the receipt of adequate results. In contrast to existing empirical estimates, the proposed algorithm allows to develop options for improving control processes. As a justification for this conclusion, conceptual options for the technical implementation of the regulatory system are proposed. In particular, a simple regulator circuit is proposed, which provides the possibility of maintaining a constant temperature of cast iron and cupola productivity by regulating the level of idle and metal charges. In this case, the process is controlled by supplying control signals to the actuators of the hopper gates with coke and a metal charge formed by manual or automated control systems.

Imaging Quality Control in the Era of Artificial Intelligence

The advent of artificial intelligence (AI) promises to have a transformational impact on quality in medicine, including in radiology. However, experience has shown that quality tools alone are often not sufficient to bring about consistent excellent performance. Specifically, rather than assuming outcome targets are consistently met, in quality control, managers assume that wide variation is likely present unless proven otherwise with objective performance data. In this article, we discuss what we consider to be the eight essential elements required to achieve comprehensive process control, necessary to deliver consistent quality in radiology: a process control framework, performance measures, performance standards and targets, monitoring applications, prediction models, optimization models, feedback mechanisms, and accountability mechanisms. We consider these elements to be universally applicable, including in the application of AI-based models. We also discuss how the lack of specific elements of a quality control program can hinder widespread quality control efforts. We illustrate the concept using the example of a CT radiation dose optimization and process control program previously developed by one of the authors and provide several examples of how AI-based tools might be used for quality control in radiology.

Human reliability study on the door operation from the view of Deep Machine Learning

Abstract Despite the advances made in developing human reliability analysis (HRA) methods, a gap remains between the methods developed by HRA researchers and the ways HRA practitioners actually use. Automated process control and control room operating supervision are important in major process industries. This paper introduces operator’s operational and cognitive errors in the process reliability analysis based on a review of current research on HRA techniques, a procedural framework of qualitative screening phase for the control rooms in the metro industry context, and panel discussions with HRA analysts, systems analysts, and control operators. HRA method Standardized Plant Analysis Risk-HRA (SPARH) based on Machine Learning is utilized to evaluate human error probabilities (HEPs) of action and diagnosis in the Door Operation of Guangzhou Metro Corporation (GMC) Line 1. The results show that improving staff’s experience/training level will greatly enhance operating reliability of door operation. Considering diagnosis complexity and situational awareness, different decision makers have different effects on the operating reliability. We suggest that the quality of ergonomics/Human Machine Interface (HMI) and the availability of existing ergonomics/HMI assessment tools should be evaluated based on industry standards.

Optimized Process Control for Operation, Energy, Management, and Maintenance in Boiler Plant Using SCADA

The purpose of this study is to design the SCADA to monitor and control the boiler operation using PLC, and to analyze the efficiency of the boiler. This paper outlines the various stages in the operation of the boiler conversions manually operated, towards the boiler operates in full. Boiler is very important part in industry and it require inspection and monitoring process continuously with a specific time interval. Earlier days this inspection and monitoring process is done with human workers. By done with human workers, there are possibilities of errors with human workers while measuring parameter values in boiler operation process. So it is needed a reliable monitoring system is required to avoid these errors and maximize profit. The automation can be easily done by using Programmable Logic Controller (PLC) and SCADA, by constant monitoring using SCADA that already connected to the PLC using communication cable. In this paper the design and automated development of some techniques used for boiler automation. Boiler automation includes the monitoring of water level, pressure and temperature using different sensors .

Process control for energy efficient operation of reactive dividing wall columns

Process integration offers great potential, both economically as well as ecologically, mainly due to high possible energy savings. However, the design and operation of integrated processes is impeded by complex process dynamics introduced by the integration of multiple unit steps in one apparatus. A prime example, the reactive dividing wall column combines a reaction and several separation steps in one column shell. The high degree of integration results in an especially challenging operation of this apparatus. Reliable design methods and efficient control structures need to be provided in order to enable the industrial application of reactive dividing wall columns. A key challenge is to ensure an energy efficient operation in case of disturbances. This study aims at understanding the underlying dependencies of the most important variables, i.e. the liquid split, the energy demand and the component distribution, regarding energy efficient control. Based on these findings a straightforward decentralized control structure is presented and analysed. The structure utilizes the liquid split as manipulated variable in order to control the component composition on the top stage of the prefractionator. The evaluation of the structure regarding energy efficient process control is carried out by comparison to a structure with fixed liquid split.

Turbidity control on dissolved air flotation process using fuzzy logic.

This study intends to explore fuzzy logic control on clean water turbidity process with dissolved air flotation (DAF). Three different strategies were tested to regulate clean water turbidity by manipulating the saturator vessel flow output aiming for low actuators control effort. Saturator pressure was the manipulated variable (MV) in the first control loop named SISO I. The second control loop, SISO II, used recycle stream valve opening as MV. The third control loop (MISO) was developed applying fuzzy logic instead PID control. Several performance criteria were used to analyze the process control performance such as integral absolute error (IAE), recycle stream valve and saturator vessel pressure regulating valve control efforts, ECV01 and ECV02 respectively. Results from SISO I and SISO II strategies showed that recycle stream is a better MV than saturator vessel pressure for clean water turbidity control in the DAF process. Only SISO II and MISO strategies proved to be capable of regulating the process variable. However, MISO control showed better performance based on IAE value against SISO II, with a reduction of 11.24% on its value, even the recycle stream valve control effort for MISO control being fairly higher than that for SISO II. Nevertheless, fuzzy logic control application gave rise to better turbidity control, and consequently prevented the excessive use of clean water in the recycling stream.

Design and Applications of Soft Sensors in Polymer Processing: A Review

In manufacturing industry, process monitoring is a key to observe the product quality, operational health, safety, and also for achieving good/satisfactory process control performance. In polymer processing, the level of control of the process operational quality is vastly dependent upon the performance of the process monitoring techniques as it is quite difficult to observe the inside of an extruder barrel (or processing chamber) during the process operation. Currently, a number of physical sensing devices/techniques are widely available for industrial applications for monitoring of parameters, such as melt temperature, melt pressure, screw/processing speed, product dimensions, and so on, in real-time. However, there may be some limitations in using physical sensors in practical process measurements (such as melt viscosity and melt temperature profile) due to several constraints such as their access requirements, disruptive effects to the melt flow, fragility, complexity, and so on. Therefore, soft sensing techniques should be highly useful for improved process monitoring and modeling, and hence for advanced process control. In this paper, a general description is presented on the state of the art of soft sensor design. Then, a comprehensive review is made on the use of soft sensing techniques in polymer processing applications while identifying their capabilities and limitations. Eventually, the importance of developing of such soft sensing techniques (together with some sort of built-on intelligence) for the advancement of process monitoring is discussed while indicating some of the possible directions for future industry.

Topological Luminophor Y2O3:Eu3++Ag with High Electroluminescence Performance.

Improving luminescent intensity is a significant technical requirement and scientific problem for the luminescent performance of fluorophor materials through the ages. The process control and luminescence performance still limit the developments of luminescent intensity even though it can be improved partly by covering or magnetron sputtering of precious metals on the surface of the fluorophore materials. On the basis of the improvement of luminescence center radiative transition rate by surface plasma resonance and Y2O3:Eu3+ microsheet phosphors, a fundamental model for topological luminophor Y2O3:Eu3++Ag was designed through referencing the concepts of topological materials to enhance luminescent performance by composite luminescence, which is composed of Eu3+ centric electroluminescence and surface plasma-enhanced photoluminescence by Ag. The topological luminophor Y2O3:Eu3++Ag was successfully synthesized with an asymmetric-discrete Ag nanocrystal topological structure on the surface just via illumination. Experimental results suggest that the luminescence performance of topological luminophor Y2O3:Eu3++Ag increased by about 300% compared to that of Y2O3:Eu3+ phosphors in the same conditions. The design of a topological luminophor provides a new approach to further improve the luminescent intensity of phosphors.

A Benders Decomposition Approach for Resilient Placement of Virtual Process Control Functions in Mobile Edge Clouds

Replacing hardware controllers with software-based virtual process control functions (VPFs) is a promising approach for improving the operational efficiency and flexibility of industrial control systems. VPFs can be executed in edge clouds in 5G mobile networks or in the wireless backhaul, which can further improve efficiency. Nonetheless, for the acceptance of virtualization in industrial control systems, a fundamental challenge is to ensure that the placement of VPFs be resilient to component failures and cyber-attacks, besides being efficient. In this paper we address this challenge by considering that VPF placement costs are incurred by reserving mobile edge computing (MEC) resources, executing VPF instances, and by data communication. We formulate the VPF placement problem as an integer programming problem, considering resilience as a constraint. We propose a solution based on generalized Benders decomposition and based on linear relaxation of the resulting sub-problems, which effectively reduces the number of integer variables to the number of MEC nodes. We evaluate the proposed solution with respect to operational cost, efficiency, and scalability in a simulated metropolitan area. Our results show that the proposed solution reduces the total cost significantly compared to a greedy baseline algorithm and a local search heuristic, and can scale to moderate problem instances.

Novel fuzzy modeling and energy-saving predictive control of coordinated control system in 1000 MW ultra-supercritical unit

In order to satisfy the growing demands of control performance and energy conservation in power generation process, a novel T–S fuzzy modeling method combined with the quantum artificial bee colony (QABC) algorithm is proposed and applied to the coordinated control system (CCS) of ultra-supercritical unit in 1000MW power plant. The T–S fuzzy modeling consists of the identifications of premise part and consequence part. In the premise part identification, the cluster number and initial cluster centers are obtained at first by using entropy-based clustering method. Secondly, the initial cluster centers are modified through QABC algorithm to guarantee the integral of data and avoid possible marginalization. Then, the consequence part is identified through exponentially-weighted least squares. Furthermore, on account of the obtained fuzzy model, an energy-saving predictive control (ESPC) algorithm based on the generalized predictive control is introduced. In the rolling optimization process of ESPC, the values of manipulated variables taken as energy consumption indicator are introduced into objective function to decrease the consumption of energy and improve the performance of control process. Meanwhile, the addition of manipulated variables constraints can obtain further improvements of energy-saving efficiency and control performance. The simulation results demonstrate the high precision of identified model and ideal performance along with energy-saving ability of ESPC.

Performance Assessment of a Boiler Combustion Process Control System Based on a Data-Driven Approach

For the requirements of performance assessment of the thermal power plant control process, the combustion control system of a 330 MW generator unit in a power plant is studied. Firstly, the five variables that affect the process control performance are determined by the mechanism analysis method. Then, a data-driven performance assessment method based on the operational data collection from the supervisory information system was proposed. Using principal component analysis technique, we found that five different variables have different degrees of effect on the performance of the combustion process. By means of qualitative and quantitative analysis, five contribution rates of different variables affecting the performance index of the system were obtained. After that, the data is normalized to the non-dimensional variable, the performance assessment index of the boiler combustion process is defined, and the classification and assessment criterion of it are given. Through using the proposed method on the operation data of the 1# boiler and 2# boiler within 1 day, the performance indexes are calculated and achieved during different time periods. Analysis of the results shows that this method will not generate additional disturbance to the normal operation of the system, and it can achieve a simple, reliable, accurate and rapid qualitative and quantitative analysis of the performance of the boiler combustion control system, and also it can be extended and applied to other multivariable control systems.

The problem of assessing the adaptive capacity of the human operator

Decomposition of professional thinking of the human operator in process control, requiring intense attention for a long time, was performed. The problem of assessing the ability of the operator to adapt to mental stress was formulated. The authors proposed a model for calculation of the indicators of the functional state of the body during the transition from normal to hard work. It is based on a dynamic model of the system, which is not in a stationary state under external forces, but will be changing until the rate of entropy production within the system reaches the minimum in these conditions. It is shown that this model can be used to solve the problem.