Process Control and Systems Commons^™

Pid Control For Lower Limb Exoskeletons: A Framework Evaluation, Javlonbek Rakhmatillaev, Vytautas Bučinskas

Chemical Technology, Control and Management

This research provides a detailed guideline for implementing and evaluating Proportional Integral Derivative (PID) control frameworks in lower limb rehabilitation exoskeleton robotics. It examines the role of control systems within rehabilitation robotics, outlines the principles of PID control, describes exoskeleton architecture, explores applications of PID control, reviews optimization strategies, presents experimental validations, and considers future developments in the field. The proposed control framework incorporates aspects of mechanical design, actuator and sensor selection, and PID-based control algorithms, thereby promoting safe, accurate, and individualized rehabilitation support. Recommendations and effective guidance for future work are also presented.

Models And Algorithms Of Control Mechanisms In Information Exchange Processes, Madina M. Fozilova, Dilshoda N. Uchqunova

Chemical Technology, Control and Management

In modern digital systems, efficient and reliable information exchange is essential for the stability of corporate systems. Traditional data management models struggle to detect and eliminate invalid, incomplete data at early stages, resulting in reduced accuracy and system inefficiency. This article proposes an advanced framework for controlling information exchange processes through the development of a Verification and Filtering algorithm. The algorithm operates within a multi-layered conceptual model that includes data input, control, validation, optimization, and decision layers. Acting as the core component, the Verification and Filtering algorithm distinguishes valid from invalid records in real time, ensuring data integrity before storage. …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

Tools To Design Algorithms For Implementing Control Over Quantum Computers, Shilpa Narashimhan, Jihan Abou Halloun, Kip Nieman, Helen Durand

Chemical Engineering and Materials Science Faculty Research Publications

Quantum computers (QCs) may find future applications within control systems that operate manufacturing processes. For application within control engineering, quantum algorithm development must be led by control engineers. However, control engineers may face challenges in designing quantum algorithms for control engineering problems. In this work, we provide several path-finding studies that leverage engineering tools such as optimization, encryption, and computational "short-cuts" toward making algorithm design for QC easier for control engineers.

Artificial Intelligence Applications For Grid-Connected Solar Inverters, Utkirjon Ubaydullaev, Sarvinoz Mirzaeva, Hasan Mustafoev

Chemical Technology, Control and Management

The increasing global demand for renewable energy has highlighted the importance of grid-connected solar inverters in ensuring efficient and stable power conversion. However, challenges such as fluctuations in solar energy generation, grid disturbances, and power quality issues necessitate advanced control strategies. The integration of artificial intelligence (AI) into solar inverters presents a transformative solution, enhancing performance, adaptability, and reliability in real-world applications.

This review explores the role of AI techniques, including machine learning (ML), deep learning (DL), fuzzy logic, and reinforcement learning (RL), in optimizing key inverter functionalities such as maximum power point tracking (MPPT), fault detection, power quality enhancement, …

Methodology Of Automated Control Of Situations In Structurally Complex Systems With Recycled Flows, Khusan Zokirovich Igamberdiev Academician, Madina Mirxalilovma Fozilova

Chemical Technology, Control and Management

This article discusses the methodology of automated management of situations in structurally complex systems with recycled flows. The need to develop a correct model for effectively controlling such systems based on scientific principles of analysis and decision-making is substantiated. Particular attention is paid to modeling, optimizing, and using digital technologies, including artificial intelligence, to improve the accuracy and efficiency of management decisions. The principles of decomposition, multi-criteria optimization, and linguistic models of fuzzy correspondence are described, which can be used in conditions of uncertainty and complexity of industrial facilities.

Developing Decision-Making Models And Algorithms To Help Prevent Emergencies, Mizaakbar Xakkulmirzayevich Hudayberdiyev, Oybek Zokirovich Koraboshev

Chemical Technology, Control and Management

The article is devoted to the solution of the scientific issue of decision-making support for the prevention and elimination of the consequences of emergency situations. The relevance of this issue is related to the need to develop a theoretical basis for optimizing the risk of adverse effects on human health and the environment in connection with emergency situations, and a predictive model for the development of emergency situations and their prevention or elimination of their consequences. The optimization of the importance measure of signs for predicting the values of the factors of fire conditions has been carried out. In addition, …

Synthesize A Neural Network Parameter Optimizer For An Adaptive Pid Controller, Nashvandova Gulruxsor Murot Qizi

Chemical Technology, Control and Management

Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters superstructuring. In the paper, the questions of optimization of PID-regulator parameters with application of methods of neural network technology are considered. A methodology for selecting the architecture of neural network optimizer designed to determine the tuned parameters of PID regulator is proposed. The algorithm of training of the neural network, with the set on the basis of the method of inverse gradient propagation is offered. The proposed improved PID-neural regulator allowed to provide stabilization of neural network operation and its trainability in the control loop …

Strategy For Predictive Control Of The Rectification Process Based On A Model Controller With A Given Forecast, Ildar Rafkatovich Sultanov

Chemical Technology, Control and Management

A method is being developed to optimize the generated controls for the multicomponent distillation process with prediction, based on predictive data with a moving horizon. The difference between this method and the classical modeling approach, in which the percentage of the degree of opening of valves installed on the output streams of the column is used as control actions, is that control occurs on the feedback principle. The proposed method is based on the use of a dynamic process model to optimize control actions in real time in order to achieve certain production targets. The essence of the MPC approach …

Development Of Dynamic Mass-Energy-Thermodynamics Constrained Hybrid Neural Network Models For Process Systems Applications, Angan Mukherjee

Graduate Theses, Dissertations, and Problem Reports (ETD)

First-principles models can provide very good predictions even for cases when there are no data at all, or data are limited in certain range of operating conditions, or for cases where data collection is infeasible. However, the development of accurate first-principles models for complex nonlinear dynamic systems can be time consuming, computationally expensive, and may be infeasible for certain systems due to lack of sufficient knowledge (information). It is also challenging to adapt first-principles models for time-varying systems. Furthermore, it can be difficult, if not impossible, to develop accurate models for some complex phenomena that are poorly understood. On the …

Application Of Evolutionary Algorithms For Optimization Of Operation Modes Of Regional Electric Power Systems, Isamiddin Khakimovich Siddikov, Oksana Vitalevna Porubay

Chemical Technology, Control and Management

The paper presents the possibilities of using evolutionary algorithms to solve the problem of optimizing the operation modes of electric power facilities in the presence of constraints in the form of inequalities and equalities. The limits of constraints have a variable character, depending on the generated and consumed energy. Existing methods used for the optimization of modes are based on general principles and approaches to optimization, which usually adapt to the specifics of the problem. In electric power facilities, optimization problems have some peculiarities, among which is the presence of multiple constraints applied to both independent and dependent variables. Many …

Hybrid Modeling For Electrochemical Systems, Luis Alejandro Briceno-Mena

LSU Doctoral Dissertations

The discovery of new materials like catalysts, polymeric films, and biomolecules, is driven by industrial needs such as improving reaction or separation selectivity, enhancing therapeutic effects on medical treatments, or reducing costs of replacement. However, deployment of these advances in industrial applications is often hindered by the lack of models needed for design and optimization. Due to the novelty of materials and devices, experimental data and first principles' knowledge are scarce, making it hard to build models either via data-driven or knowledge based approaches. In this context, a way to efficiently combine domain knowledge with data could provide a pathway …

Modular Supply Network Optimization Of Renewable Ammonia And Methanol Co-Production, Benjamin Akoh

Graduate Theses, Dissertations, and Problem Reports (ETD)

To reduce the use of fossil fuels and other carbonaceous fuels, renewable energy sources such as solar, wind, geothermal energy have been suggested to be promising alternative energy that guarantee sustainable and clean environment. However, the availability of renewable energy has been limited due to its dependence on weather and geographical location. This challenge is intended to be solved by the utilization of the renewable energy in the production of chemical energy carriers. Hydrogen has been proposed as a potential renewable energy carrier, however, its chemical instability and high liquefaction energy makes researchers seek for other alternative energy carriers. Ammonia …

Optimal Design And Operation Of Integrated Hydrogen Generation And Utilization Plants, Ijiwole Solomon Ijiyinka

Graduate Theses, Dissertations, and Problem Reports (ETD)

There are considerable efforts worldwide for reducing the use of fossil fuel for energy production. While renewable energy sources are being increasingly used, fossil fuel still contribute about 80% of the energy used worldwide. As a result, the level of CO₂ is still increasing fast in the atmosphere currently exceeding about 410 parts per million (ppm). For reducing CO₂ build up in the atmosphere, various approaches are being investigated. For the electric power generation sector, two key approaches are post-combustion CO₂ capture and use of hydrogen as a fuel for power generation. These two solutions can also …

Techno-Economic Analysis And Optimization Of Hydrogen And Mechanical Energy Storage Systems, Pavitra Senthamilselvan Sengalani

Graduate Theses, Dissertations, and Problem Reports (ETD)

The increasing significance of renewable energy sources is thrusting the load cycling of fossil-fueled power plants (FFPP), designed to operate under nominal-load conditions. Integration of energy storage systems (ESS) with the FFPPs such as hydrogen energy storage (HES) and mechanical energy storage facility such as compressed air energy storage (CAES) shows the potential to minimize the levelized cost of electricity during high demand scenarios and also minimize the negative impacts of off-design FFPP operation. The deployment of energy storage facilities at the FFPP level have considerable potential advantages as they can be exploited within the existing equipment items and facilities …

Optimization And Analysis Of A Styrene Production Process, Alexis Kimpel

Honors Theses

The production of styrene from ethylbenzene in Unit 500 is designed to start up in 2024 and operate for 12 years after startup. The engineering team was tasked with designing the process, creating an economic model, and optimizing the net present value (NPV). The process was simulated in AVEVA PRO/II Simulation for the design process and to estimate the size of equipment. Parametric and topological optimization was performed subsequently on the unit operations in the process. The NPV was improved by $423M from a base case of -$919M to an optimized case of -$496M. The project is recommended to continue …

Design, Analysis, And Optimization Of A Process To Produce Dimethyl Ether From Methanol, Thomas Mathwig

Honors Theses

The company that the engineering team works for is facing a contract loss with one of its customers of methanol. To avoid economic losses, a process has been proposed for converting the unused methanol to dimethyl ether (DME) through a dehydration reaction. After a preliminary simulation of the base case and optimization of the distillation column, an Equivalent Annual Operating Cost (EAOC) of $140,000 was calculated for the column. The EAOC was the sum of the annualized capital investment and the annual operating cost. Determining the process to be worth pursuing, a Toller was brought in to provide rental equipment …

Process Modeling And Techno-Economic Analysis Of Micro- Encapsulated Carbon Sorbents (Mecs) For Co2 Capture In A Fixed Bed And Moving Bed Reactors, Goutham Kotamreddy

Graduate Theses, Dissertations, and Problem Reports (ETD)

Carbon capture, utilization, and storage (CCUS) is seen as a suite of technologies to curb the carbon dioxide emissions from the atmosphere and plays a crucial role to meet the net zero emissions target for many countries by 2050. One of the major sources for CO₂ emissions is combustion of fossil fuels. Various innovative capture technologies are being explored because the state-of-the-art monoethanolamine (MEA) based carbon capture technology has drawbacks such as corrosion, energy penalty. There are several potential solvents that have lower energy penalty, but they are highly viscous or may turn into solid phase in the absorber …

Machine Learning Based Applications For Data Visualization, Modeling, Control, And Optimization For Chemical And Biological Systems, Yan Ma

LSU Doctoral Dissertations

This dissertation report covers Yan Ma’s Ph.D. research with applicational studies of machine learning in manufacturing and biological systems. The research work mainly focuses on reaction modeling, optimization, and control using a deep learning-based approaches, and the work mainly concentrates on deep reinforcement learning (DRL). Yan Ma’s research also involves with data mining with bioinformatics. Large-scale data obtained in RNA-seq is analyzed using non-linear dimensionality reduction with Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP), followed by clustering analysis using k-Means and Hierarchical Density-Based Spatial Clustering with Noise (HDBSCAN). This report focuses …

Thermodynamic Analysis And Optimization Of Organic Rankine Cycles Using 1-Butylpyridinium Tetrafluoroborate As A Geothermal Fluid, Kazemi Shabnam

Student Works (2020-2029)

Organic Rankine cycle (ORC) is a promising technology for electricity generation by utilizing heat sources at low to moderate temperature that ranges between 80-350° C. In the present work, an ionic liquid (1-butylpyridinium tetrafluoroborate, C9H14NBF4), a known green chemical and non-volatile compound with good thermal and chemical stability, significant heat capacity, low vapor pressure and a wide liquid temperature range (25 to 459 °C), is utilized as a geothermal fluid. Simulation and optimization were conducted for a basic organic Rankine cycle (basic ORC), a regenerative organic Rankine cycle (RORC) and a two-stage evaporative organic Rankine cycle (TSORC) by maximizing exergy …

Process Control Of Activated Sludge Treatment, Phase Ii, Richard I. Kermode, Robert W. J. Brett, Joseph D. Pault Jr.

KWRRI Research Reports

Material balances on substrate and microorganisms were derived in conjunction with various mixing configurations thought to accurately describe the activated sludge process. These models include the completely mixed with bypass, plug flow, and plug flow with bypass. Two sets of kinetic mechanisms for substrate utilization and bacterial growth were employed.

A feed forward controller was designed from linear approximations of the material balances derived in the completely mixed with bypass mixing model. Utilizing frequency response methods, the controller was found essentially identical to a completely mixed modeled controller developed in a prior investigation.

Through computer simulation the controller's effectiveness was …