Process Control Model Research Articles

Cell Culture Media and Raman Spectra Preprocessing Procedures Impact Glucose Chemometrics

ABSTRACTDeployment of process analytical technology tools such as Raman or IR spectroscopy and associated multivariate calibration models for process monitoring and control plays an important role in process automation and advanced manufacturing of pharmaceuticals. Preprocessing or preparation of the spectroscopic data is an important step in developing a multivariate calibration model. There are several ways available to preprocess the data and each may influence the calibration model performance differently. Here we investigated the influence of preprocessing procedures on the development and performance of the chemometric models to predict the glucose concentration in a bioreactor. Box–Behnken design of experiment (DOE) was used to generate the Raman spectroscopy data. Four factors were considered critical in the DOE—glucose, glutamine, glutamic acid, and antifoam concentration. Raman spectroscopy data were collected both with and without aeration conditions, independently from three cell culture media. For each medium, data consisted of calibration set (27 conditions) and model validation set (9 conditions) separately. Additionally, Raman data was also collected for certain DOE runs with increasing concentration of cell densities ranging from 0.5 × 10 E06/mL to 30 × 10 E06/mL under aerating conditions. Data from the three cell culture media were used separately to develop calibration models that used four different preprocessing procedures, namely, baseline correction (BLC), Savitzky–Golay smoothing (SGS), Savitzky–Golay derivative (SGD) and orthogonal signal correction (OSC). The preprocessing procedures were applied individually and in combinations to evaluate the calibration model parameters and the performance metrics. We further developed glucose calibration models based on partial least squares (PLS) regression with 1–3 principal components. The models developed with OSC procedure gave superior performance metrics with just one principal component across all three media. Models developed with other preprocessing procedures required two or more principal components to give comparable performance. Overall, the choice of preprocessing procedures affected the model performance.

CONTROL MODEL OF THE NATURAL GAS PURIFICATION PROCESS

The equipment used in the processes of cleaning and drying natural gas for transportation and consumption and the methods applied for drying gas were comparatively analyzed, it was noted that the absorption method is more widespread among these methods due to its simplicity and efficiency, it was shown that drying gas should be carried out immediately after its cleaning from mechanical and other impurities so that during the transportation and use of gas, there are no blockages in pipelines, malfunctions in measuring devices and other gas equipment. Drying gas prevents the formation of hydrates in pipes due to condensation during transportation and increases the heat-generating capacity of gas by burning it well. The characteristics of the liquid absorbent used to dry gas in the absorption process - the absorbent, being regenerated and returned to the gas drying process were investigated, and the parameters and quality indicators affecting the technological process were determined for optimal control of the gas drying technological process at normal temperature and pressure for that process, as well as the output parameters of the gas drying unit. It was noted that although the pneumatic control system is considered morally obsolete, some elements and devices of this system, in particular, pneumatic actuators, are distinguished by their reliability in mining and field conditions. Therefore, it is advisable to use them in modern control systems with controllers. For this purpose, the structure of the general control system of the process, the information model of the control object was built, the input and output parameters, the limiting conditions for reflecting the exciting and controlling effects in the structure of the mathematical model were determined, and the limit ranges containing the minimum and maximum values of each parameter were determined. The control problem can also be formulated and solved as a problem of stabilizing productivity or another parameter. An optimal control model of the gas drying process was built, and the optimization criterion was selected. In the problem of optimizing the technological process of gas drying, the maximum productivity of the plant was adopted as the main criterion, and the humidity of the gas was adopted as the limiting condition. Several recommendations are given for solving this problem. Keywords: gas drying, absorbent, control system, optimal control, model.

Encrypted fully model-free event-triggered HVAC control

This paper addresses the critical issue of privacy and high computational overhead in cloud-based HVAC control systems for building automation. Cloud computing platforms can infer sensitive information, such as occupancy, from sensor data, making privacy a major concern for users of intelligent buildings. Existing model-based control methods ensure privacy by introducing homomorphic encryption but they rely on approximating building dynamics and are resource-intensive, leading to increasing communication and computation costs. To solve this, we propose an encrypted fully model-free event-triggered control framework that guarantees privacy while minimizing both communication and computational costs without the need for approximating building dynamics. Our approach includes a model-free controller that regulates indoor temperature and CO2 levels, and an event-triggering mechanism that optimizes communication by only transmitting data when necessary. The numerical results from the simulations using the TRNSYS platform show that our method reduces communication between the system and cloud by 64% and decreases computation time by 75% compared to the latest encrypted HVAC control method. The primary contribution of this work is the novel model-free design that simplifies the control process modeling while significantly reducing resource demands, and provides a scalable and cost-effective solution for cloud-based HVAC control, enhancing both privacy and operational efficiency in smart buildings.

CUSUM charts utilizing reparametrized Birnbaum-saunders model for fault detection and process control

Statistical process control is always intrigued by the design of effective control charts for monitoring production processes and determining assignable causes of variations. It can be challenging to keep track of a positive asymmetric response variable while considering the impact of the input variables. The current work incorporates the Reparametrized Birnbaum Saunders (RBS) model to develop more effective cumulative sum (CUSUM) control charts for analyzing the mean of such a process. We perform a simulation study to evaluate the effectiveness of existing and derived approaches in terms of run length characteristics. The findings showed that when the underlying process distribution is positively asymmetric, the proposed charts provide stronger protection against process alterations as compared to existing methods. Moreover, the suggested control charts are implemented using actual data from a combined cycle power plant.

Optimization of emergency frequency control strategy for power systems considering both source and load uncertainties

With the increasing integration of renewable energy sources and the presence of numerous controllable loads such as electric vehicles and energy storage in the modern power system, higher nonlinearities and uncertainty both sources and loads are introduced. These factors pose challenges in achieving fast and accurate emergency frequency control. Therefore, this paper addresses the issue of dual source-load uncertainties in power system and presents an optimization strategy based on the Soft Actor Critic (SAC) algorithm that involves the participation of controllable loads in emergency frequency control. Firstly, the spatio-temporal uncertainties of wind farm power output on power supply side and power demand on the load side are described using Weibull and normal probability distributions, respectively. Secondly, an improved Markov Decision Process (MDP) model for emergency frequency control is established, which considers the characteristics of the dual source-load uncertainties. Finally, an optimization of the SAC algorithm is conducted based on Deep Reinforcement Learning (DRL), aiming to achieve rapid system frequency recovery and minimize the cost of removing controllable loads. The presented approach in the paper enhances the emergency frequency control strategy for uncertain power systems and effectively addresses the issue of source-load uncertainty compounded by fault power shortages.

Joint optimization of condition-based production and maintenance with mutual production-deterioration dependencies

Production control and maintenance scheduling are the two major cost-effective methodologies in the manufacturing systems. The existing literature considers these processes in isolation or with unidirectional dependencies. To fill this gap, we propose an integrated control model of maintenance and production process considering the mutual influence between production and deterioration in a single-machine system. By leveraging condition monitoring data, we enhance decision-making effectiveness and develop a comprehensive framework based on an infinite-horizon Markov decision process. We establish and prove the monotonic structural properties of total profit and maintenance actions relative to machine state, and develop an improved value iteration algorithm to reduce the computational space. Numerical experiments demonstrate the performance of our model in maximizing profits compared to traditional condition-based maintenance and condition-based production approaches. Sensitivity analysis further highlights key parameters influencing optimal policies, providing valuable insights for practical applications.

Industrial Metaverse-Based Intelligent PID Optimal Tuning System for Complex Industrial Processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

Spanish-English Code-Switching and Cognitive Control

Code-switching is the act of alternating between multiple languages within a conversation, common amongst bilingual speakers. The Control Process Model claims that different types of language contexts like code-switching involve different levels of engagement of cognitive control. In studying this phenomenon, prior research has demonstrated that reading code-switched sentences enhanced cognitive control (conflict resolution) on non-verbal trials compared to one-language contexts. The current study investigates how code-switching affects conflict resolution similar to that of the previous study but using more ecologically valid auditory stimuli (speech). Participants are Spanish-English bilinguals in the US who learned both languages before the age of 12. The experiment alternates one-language spoken sentences and Spanish-English sentences. Sentences are interleaved with Flanker trials, congruent or incongruent. Participants respond to the direction of the middle arrow. The prediction made is that both the presence of a code-switch and the type of code-switch modulates the conflict effect. Results show that the presence of a switch and the type of switch matters in modulating cognitive control in bilingual individuals.

Multi-Stage Corn-to-Syrup Process Monitoring and Yield Prediction Using Machine Learning and Statistical Methods.

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach.

Information and mathematical model of quantum communication channel state control processes

The most protected and stable communication systems today are quantum channels of information transmission and processing. Thanks to the unique properties of photons as information elements, it becomes possible to monitor and analyze the state of information flows in communication or information transmission channels. Physical attributes such as spin, polarization, radiation frequency, phase synchronization, and the quantum entanglement effect can be tracked and interpreted online to improve the quality and reliability of information in computer systems. In order to effectively use information in support or decision support systems, it is necessary to carefully formalize the processes and indicators of quantum systems for the creation of information processing and transmission, for which information and mathematical models should be created that describe the state of the quantum communication channel (QCC).The information model should allow the convolution of the information space. The mathematical model must prove the processes of tracking the states of quantum information and provide a description of the phase state of indicators of the quantum environment. A lock in a closed space with established cause-and-effect relationships is equal to a system of clear logic.The authors summarize the experience of developing and implementing the method of simulated dynamic modeling of events in an abstract communication channel, which allows formalizing and classifying cause-and-effect relationships of quantum carriers in the analyzed channels. It is proposed to use a unified neural network for the organization of SPPR in quantum-mechanical information transmission systems. Such a network could provide an automatic intelligent system state analysis mode. Such an analysis makes it possible to classify the aggregates of current system parameters to the level of diagnostics of the state of information flows and conclusions based on such diagnostics with the support of decision-making about the quality and reliability of the transmitted information. Such a system, working in OLAP (Online analytical processing) mode, could automatically manage the process of generation and transmission of information, reacting without human intervention to emerging critical errors or attempts at unauthorized system hacking. The observer effect leads to the fact that an attempt to measure the state of a photon inevitably causes an almost instantaneous change in this state. Attempting to parallelize a photon has the same consequences. This cannot be unnoticeable during further authorized acceptance of information. The analyzed quantum communication channel (QCM) consists of a set of technological elements distributed in space. The channel works in its own time, which is formed by clock pulses and creates a flow of information.

Визуальная интерпретация парадигмы управления организационной системой c разнородными производственными ресурсами

The object of the study is developing a formalized mechanism for managing an organizational system that implements complex production activities, including different types of industrial production, and applies the corresponding heterogeneous resources. The subject of the article is an important intermediate stage of this development, which is an abstract interpretation of the optimal management paradigm of limited resources of such an organizational system, proposed earlier by the authors. The interpretation will allow reasonably forming a strict mathematical model of objects and control processes. The management mechanism in any subject area, providing for adopting management decisions by people authorized to do so, must have an ergonomic development and appropriate information support for these decisions. The so-called result information plays the role of such support, that is the information on the characteristics of the current and planned states of the management object in the problem being solved which is the productivity of the organizational system resources. It seems advisable to carry out such ergonomic and information development of the designed problem-oriented management mechanism in the form of a visual interpretation of its substantive concept. The prototype of such interpretation tools is graphic images used in modelling and designing technical systems, developed in this work in the field of management in organizational systems with heterogeneous resources. The article provides examples of their application, demonstrating the possibility of visual representation of the current state of the organizational system and the set of its possible controlled states.

Dynamic Neural Network Predictive Compensation-Based Point-to-Point Iterative Learning Control With Nonuniform Batch Length.

This article discusses the problem of nonuniform running length in incomplete tracking control, which often occurs in industrial processes due to artificial or environmental changes, such as chemical engineering. It affects the design and application of iterative learning control (ILC) that relies on the strictly repetitive property. Therefore, a dynamic neural network (NN) predictive compensation strategy is proposed under the point-to-point ILC framework. To handle the difficulty of establishing an accurate mechanism model for real process control, the data-driven approach is also introduced. First, applying the iterative dynamic linearization (IDL) technique and radial basis function NN (RBFNN) to construct the iterative dynamic predictive data model (IDPDM) relies on input-output (I/O) signal, and the extended variable is defined by a predictive model to compensate for the incomplete operation length. Then, a learning algorithm based on multiple iteration errors is proposed using an objective function. This learning gain is constantly updated through the NN to adapt to changes in the system. In addition, the composite energy function (CEF) and compression mapping prove that the system is convergent. Finally, two numerical simulation examples are given.

Transforming Tofu Quality Control: Integrating Statistical Process Control, Ishikawa, and Interpretive Structural Modeling for Superior Outcomes

In a highly competitive market, maintaining high product quality is essential for maintaining customer satisfaction and loyalty. Producers are famous in East Java and face the challenge of ensuring consistent quality products through strict production processes. Know that as easy products broken and soft, needy control of careful quality to meet hope consumers about freshness, texture, and quality in a way whole. Although traditional control methods often fail to overcome the production complex that causes a disabled product. This research proposes integrating Statistical Process Control (SPC), Ishikawa diagrams, and Interpretive Structural Modeling (ISM) to improve control quality. SPC makes it possible to monitor and control production processes in real-time, identify deviations, and repair deviations. However, the SPC limitations include a focus on quantitative data and post-incident detection problems. To overcome root problems, the Ishikawa diagram categorizes the reasons for potency as material, machine, method, power work, and environment. ISM prioritizes action repair based on impact and relationship. Approach This integrated approach provides comprehensive solutions to improve the quality of knowledge. In this study, the control process quality was evaluated using SPC, the cause of defects was identified using Ishikawa diagrams, and priority action repair was performed via ISM. Findings show that SPC directly effectively monitors process control, the Ishikawa diagram identifies main defects, and ISM prioritizes impactful action improvements, emphasizes excessive additions to subtraction materials, and improves health workers’ and mixing process material standards. Approach integrated possible identification of additional problems and improved the quality strategic makes a significant contribution to enhancing product quality knowledge.

3D Modelling and Simulation Based on Kinematic Analysis for Hippotherapy

The engineering perspective on the physiology and anatomy of living things has led to the emergence of the science of biomechanics. Developing computer technologies have made it possible to examine the behaviour of living things in more detail and to develop technologies that enable kinetic and kinematic analysis of movements to imitate real limbs. In this study, the natural gaits of horses (Walk, Canter and Trot) were analysed with image processing techniques, kinematic analyses were made and three-dimensional solid models were created. Giving movement ability to the extracted solid models was carried out with the simulation software prepared on the Matlab-SimMechanic platform. By comparing the findings obtained as a result of image processing and simulation, the success of transferring the movements to a developed system was evaluated. After mathematical equations were created in the MATLAB environment, the equations were tested on the developed system and their performance rates were determined. With the developed system, joint angles, ability to reach the correct position and movement ability were analysed and it was shown how the mathematical equations required to perform the movements could be derived. As a result, a design process model for movement analysis, simulation and control of different living groups is presented with the equations obtained by formulating the information obtained from the developed system.

About the use of machine learning in modeling business processes

In a highly competitive environment, as well as the activation of the domestic production market, enterprises need to quickly adapt to modern conditions. There is an obvious increase in the number of small manufacturing enterprises that participate in tenders on electronic trading platforms and offer their services to large enterprises, especially this growth is noticeable in the field of militaryindustrial complex. Customers prefer to cooperate with small enterprises that are adaptable to the order conditions and have not only short terms of order fulfillment, but also a flexible pricing system due to low administrative and bureaucratic costs. At the same time, such enterprises have problems with the organization of business processes when the volume of orders grows. In this paper, the authors have built a model of the Quality Control process using BPMN method on the basis of small enterprise practice. This model can be the basis for training a machine learning system to build a model of business processes. Natural language text processing is proposed as an area of artificial intelligence, which allows enterprises to use this unified technology to reduce the cost of developing and describing business processes.

SIMULATION OF AN AUTOMATIC CONTROL SYSTEM FOR A GRINDING MACHINE

This article discusses the MATLAB and MasterSCADA programs and the possibility of their joint application. To solve this problem, we used packages Fuzzy Logic Toolbox, Neural Network Toolbox, used in MATLAB that allow us to implement a process control model. The program implements the possibility of providing communication with the OPC Toolbox SCADA system, which allows for the interaction of system components using the OPC protocol. A block diagram of the control system of the pulverised unit has been developed, which acts on the control object through actuators. To implement an automated control system for the crumpling process, the following are offered: MATRIX-E/DUPLEX Q412/A near-infrared spectrophotometer, ARIES PLK controller, ARIES KIPPRIBO frequency converters

Statistical parameterized physics-based machine learning digital shadow models for laser powder bed fusion process

This paper presents a statistical physics-based machine learning model for predicting defects, such as surface roughness and lack-of-fusion porosity, in the laser powder bed fusion of metals (PBF-LB/M) additive manufacturing process. The statistical physics-based model is calibrated and validated against controlled single-track experiments and used for statistical prediction for multi-layer and multi-track cases for PBF-LB/M defects. A mechanistic reduced-order-based stochastic calibration process is introduced to capture the stochastic nature of the melt pool. The calibrated physics-based digital shadow model is demonstrated for predicting the surface roughness of the National Institute of Standards and Technology (NIST) overhang part X4, with a difference of 9.3% compared to the experimental results. By leveraging data obtained from both the physics-based model and experiments, a machine learning model has been trained for fast predictions (inference time of 0.4 ms) with high accuracy (error bound of 6.7%). This model can predict melt pool geometries under various processing conditions, offering a control strategy for the PBF-LB/M process. Further, the trained machine learning model is showcased to demonstrate a control application of melt pool geometries (width and depth) for specific processing parameters. These developed models (physics-based and machine learning) serve as a digital shadow of the PBF-LB/M process, offering predictive capabilities to build a digital twin model for process control, optimization, and online monitoring.

Modulating bilingual language production and cognitive control: how bilingual language experience matters

Abstract The Adaptive Control Hypothesis and the Control Process Model propose that bilingual language use in different interactional contexts requires control processes that can adapt in different ways to linguistic demands. This study explored the effects of language experience on cognitive flexibility and inhibition among 41 Chinese–English bilingual adults. In particular, it aimed to investigate the relationship between spontaneous language production (i.e., bilingual conversation and narration tasks) and cognitive control. Participants’ inhibitory control and cognitive flexibility efficiency was measured through verbal and spatial Stroop tasks, and a colour-shape switching task. Overall, it showed that frequent practices of intersentential switching in speech production resulted in significant facilitatory effects in both verbal and nonverbal inhibitory control. This study provides new evidence for the importance of bilingual language experience in adaptive cognitive control in naturalistic speech production and furthers our theoretical knowledge of the relationship between the language system and crucial domain-general cognitive processes.

Transformer fault analysis based on rough sets and improved gravity algorithms

Transformer is one of the most important equipment of power grid, in order to ensure the safe and reliable operation of power grid, the correct diagnosis of transformer fault is very important. Rough set theory is a new field of mathematical science, and it is applicable to many fields, including pattern recognition, machine learning, decision support, process control, and predictive models. Considering the incompleteness and complexity of transformer fault diagnosis data, a fault diagnosis method based on rough set theory and improved gravity algorithm is proposed in this paper.

Attribute statistical process control under nonconstant process deterioration

AbstractA statistical process control (SPC) model is introduced that incorporates sample data on the number of defectives and allows the probability that an assignable cause of variation in each time interval of a finite production process to be nonconstant. A Limited Memory Influence Diagram (LIMID) model is implemented to facilitate the decision in each time interval on whether or not to investigate the potential presence of the assignable cause of variation and restore the process to working condition. The method determines control limits that minimize the costs of inspection, repair, sampling, and operating under the assignable cause of variation. Sample size and sampling intervals can also be adjusted to further reduce the costs of maintaining quality control. This method expands on the capabilities of models that assume that the conditional probability of an assignable cause occurring remains constant throughout the production horizon.