Heating Control System Research Articles

Design and Development of Thermal Management System (Tms) for Battery Packs of Electric Vehicles- A Review

A combination of their high density of energy and extended cycle life, lithium based batteries are frequently used in electric source vehicles. Maintaining the right temperature range is crucial since lithium battery performance and lifespan are very temperature-sensitive. In this context, this research offers an efficient battery heat control system solution. This essay examines lithium battery heat generating phenomena and important thermal concerns. The numerous battery temperature management system studies are then carefully examined and arranged in groups based on available thermal cycle possibilities. Air cooling in the cabin, liquid cooling in the second loop, and two-phase direct refrigerant cooling are all part of the Battery temperature control system with a vapor compression cycle. Cooling via Symmetrical phase transition, heat pipes, and thermoelectric elements are all part of the technique for managing battery temperature without a vapor compressed system. For each temperature management system, the ideal temperature and variation to the maximum in temperature of the batteries are evaluated; furthermore, applicable for the Battery Temperature Manage System that lessens the shortcoming of every system is brought up. As an achievable heat management solution for batteries made of lithium with substantial energy densities, finally, a unique system for monitoring battery temperature is proposed.

DEVELOPMENT OF A PROCESS AUTOMATION SYSTEM FOR HEATING, VENTILATION AND AIR CONDITIONING FOR THE FOOD INDUSTRY ON THE BASIS OF HONEYWELL EQUIPMENT

Currently, the development of industrial automation makes it possible to implement high-precision control systems that consider the dynamic properties of complex objects. The construction of distributed control systems based on modern software products provides decentralized management of technological processes. The modernization of existing control systems with the help of modern industrial equipment makes it possible to increase the productivity of enterprises and safety at work. This study is devoted to the development of an automated control system for heating, ventilation, and air conditioning processes for the food industry. In this study, a heat exchanger was selected as the control object. A mathematical model of the control object for stability, controllability, and observability was investigated. A PID regulator was synthesized, and its coefficients of the PID regulator were obtained. A comparative analysis of the behavior of the system dynamics at different regulator coefficients was carried out. The results of the modeling and experiments were carried out using real industrial equipment at the Honeywell laboratory at JSC KBTU. Software implementation was carried out using the Experion PKS distributed control system. The configuration of the C300 controller is presented. A Safety Instrumented System (SIS) was developed for the safe and trouble-free operation of the system. SIS was also developed using the Safety Manager and Safety Controller tools. Risk reduction factors (RRF) and Safety Integrity Level (SIL) were calculated and analyzed. A process-controlled mnemonic was developed.

AHU sensor fault diagnosis in various operating conditions based on a hybrid data-driven model combined energy consumption

Data-driven methods has the inspiring potential for sensors fault detection in heating, ventilation, and air-conditioning (HVAC) intelligence control system. However, the interpretability of pure data-driven methods is relatively poor. Moreover, pure data-driven methods heavily rely on quality of HVAC sensor data that causes pure data driven methods have inconsistent detection efficiency in various operation conditions and sensor locations, resulting in poor general applicability. To solve above problems, an enhanced data-driven model based on energy consumption correlation (ECC) is proposed. First, the collected data from sensors are collected in chronological order and undergo data preprocessing techniques for noise removal. Subsequently, the energy consumption data is utilized to differentiate the operating conditions of the HVAC system. Simultaneously, a mapping relationship is established between sensor data and energy consumption. It uses the mapping relationship to construct the ECC for faulty sensor detection. Finally, the diagnosis of air handling unit (AHU) sensor faults is achieved by employing the hybrid data-driven model based on the ECC. The experiment conducted a comparison between the hybrid data-driven model and two novel pure data-driven methods. The results of the experiment demonstrate that the hybrid data-driven model exhibits excellent fault detection efficiency across different operating conditions and sensor locations. The fault alarm rate of the hybrid data-driven model is 0–3.26% under various AHU sensor fault conditions. And the fault diagnosis rate of the hybrid data-driven model is 96.23% and 96.11% under single-operation condition and dual-operation condition, respectively. Furthermore, compared to pure data-driven methods, the hybrid data-driven model is less affected by data quality and possesses a broader applicability.

RESEARCH OF STRUCTURAL AND MECHANICAL PROPERTIES OF MEAT AS AN OBJECT OF PROCESSING IN MEAT COMMINUTOR

This work is dedicated to the study of the energy efficiency of using electric heating systems in the classrooms of Ivano-Frankivsk National Technical University of Oil and Gas (IFNTUOG), where the actual state of heating systems and their efficiency in educational building №9 were determined. An experimental study was conducted, which included determining the heating and cooling time of working surfaces and the distribution of temperatures across them in a classroom; determining the distribution of thermal energy at several levels of the room. Experimental data were obtained to evaluate the effectiveness of different electric heating systems in classrooms in standby mode, which allowed selecting the infrared heating system based on ceiling heaters as the most optimal technical solution for providing an acceptable level of comfort and energy efficiency. The effectiveness of using air thermostats for the electric heating system based on infrared heaters, which operates in standby mode, leading to excessive electricity consumption, was also studied. The results confirm that the heating system using infrared panels ensures even heat distribution, maintains comfortable temperature and humidity conditions, and is distinguished by high energy efficiency. In contrast, the use of electric convectors with an inefficient water heating system does not provide adequate air heating, especially in spacious and high rooms, where heat dissipates quickly due to insufficient insulation. The data obtained during the scientific research, along with previous studies, allow for the development of an automatic intelligent heating and ventilation control system that will use the most advanced research results in the field of efficient operation of engineering systems in educational institutions.

Artificial Intelligence Techniques based on aquaculture solar thermal water heating system control

The One of the most promising applications of hot water is aquaculture. Temperature is one of the most principle parameters affects aquaculture life. It can cause stress and mortality or superior environment for growth and reproduction. Shrimp is warm water aquaculture type need hot water supply. To increase pond production it is necessary to keep water temperature at 34oC. Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and are becoming more and more popular nowadays. In this paper a control system using artificial neural network control (NNC) either adaptive fuzzy logic control (AFLC) are design to control the hot water temperature. A comparison study is applied between the performance of FLC and NNC. The performance of NNC is the best because the control design takes into consideration different variables which give an accurate output than FLC. Also this paper introduces a complete mathematical modeling and MATLAB SIMULINK model for the proposed aquaculture heating system.

Successful application of predictive information in deep reinforcement learning control: A case study based on an office building HVAC system

Reinforcement Learning (RL), a promising algorithm for the operational control of Heating, Ventilation, and Air Conditioning (HVAC) systems, has garnered considerable attention and applications. However, traditional RL algorithms typically do not incorporate predictive information for future scenarios, and only a limited number of studies have examined the enhancement and impact of predictive information on RL algorithms. To address the issue of coupling RL and predictive information in HVAC system operation optimization, we employed an open-source framework to examine the impact of various predictive information strategies on RL outcomes. We propose a joint gated recurrent unit (GRU)-RL algorithm to handle situations where a time-series exists in state space. The results from four classic test cases demonstrate that the proposed GRU-RL method can reduce operating costs by approximately 14.5% and increase comfort performance by 88.4% in indoor comfort control and cost-management tasks. Moreover, the GRU-RL method outperformed the conventional DRL method and was merely augmented with prediction information. In indoor temperature regulation, the GRU-RL algorithm improves control efficacy by 14.2% compared to models without predictive information and offers an approximately 5% improvement over traditional network models. Finally, all models were made open source for easy replication and further research.

Big data information collection of IoT for heat storage heating control system

In order to understand the application of iot Big data information acquisition for heating control systems, a research on iot Big data information acquisition for thermal storage heating control systems is proposed. This paper first analyzes the shortcomings of the traditional heating system, the heating data is scattered and difficult to collect. The IoT technology is applied to the heating platform through data acquisition, data visualization, data mining, data management and other aspects. Secondly, the application of the IoT in heating system data acquisition is studied, including the types of heating data collected, data acquisition process, data acquisition standards, etc. Finally, this paper takes the energy-saving transformation of 26 boilers in a heating enterprise as an example. The research results show that the boiler efficiency obtained in real time by using the online monitoring system of this project is 95.36%, and the reverse balance boiler efficiency in the paper report of the special inspection office is 94.88%, with a deviation of 1.48%. The application of the IoT technology can obtain the boiler indicators in real time and effectively, and can realize the comprehensive analysis of the monitoring data, and realize the evaluation of the energy efficiency operation state of the gas boiler and the fault diagnosis and early warning. The IoT technology is an effective means of fine energy-saving management in heating enterprises.

Intelligent monitoring and optimal control of HVAC system and its cloud-edge implementation

In this work, the solution of the intelligent monitoring and optimal control of a real-world Heating, Ventilation, and Air Conditioning (HVAC) system is presented with its cloud-edge implementation. The main contribution and novelty of this work are three-fold: (1) an improved just-in-time-learning aided canonical correlation analysis (JITL-CCA) based health monitoring method is proposed; (2) a hybrid particle swarm-based optimization control method to improve the energy efficiency of HVAC systems is proposed; (3) a cloud-edge platform is developed for its implementation. Particularly, this platform allows online verifying and implementing the monitoring and optimal control methods of real HVAC systems via Internet, which is significantly different from the simulation-based and the test rig-based verification ways. Such a verification is available at: www.acain.cn.

A Comparison of Classical and Reinforcement Learning-based Tuning Techniques for PI controllers

This study compares two tuning techniques for Proportional-Integral (PI) controllers. The first strategy uses the Pole-Zero Cancellation method, a well-established technique in the field of dynamic systems control. The second strategy introduces an innovative approach by using a reinforcement learning-based technique to adaptively tune a PI controller. To compare these tuning methodologies, a Heating, Ventilation and Air Conditioning (HVAC) control system was selected as a case study to guarantee users’ thermal comfort. In both cases, the HVAC process was modelled as a first-order system without time delay. In addition, the performance of the proposed controllers analysed by evaluating temperature set-point tracking and disturbance rejection caused by the occupancy of people in the room. The results demonstrate that classical methods are efficient and quick to implement, while the use of RL also enables optimization of energy consumption and reduction of operating costs.

Adaptive plc intelligent control based on the integrated system of smart grid thermal energy conversion

During the construction of smart grids, due to technological limitations, traditional sequential control systems have been unable to meet the needs of grid operation, exposing problems such as single control methods and functions, low operational efficiency, inability to independently complete complex tasks, and poor anti-interference ability, which in turn affect the power supply quality and operational stability of the grid, and fail to truly achieve the reliability, safety, and reliability of smart grids Economic and efficient goals. For this purpose, the author describes an adaptive intelligent control method for an integrated biomass energy heat conversion system consisting of more than one suction biomass gasifier, biomass gas burner, and steam boiler. The system uses PLC as a tool and takes the boiler outlet steam pressure as the control parameter. Based on the changes in the boiler outlet steam pressure, it automatically adjusts the feed and gas production of the gasifier, and subsequently adjusts the air supply of the burner. Ultimately, it achieves the goal of automatically adjusting the boiler heating volume with changes in steam pressure. The integrated heating and adaptive intelligent control system based on biomass gasification has been reliably verified in industrial applications.

Design of Heater Control Equipment in Interface Mill Product Part Separator Process

The interface mill product's indication of the supporting components reveals a surplus of flaws, leading to the waste of plastic and iron materials. Black spots and holes are the two main forms of defect criterion finds, and both require correct handling to prevent long-term material and financial consequences. The equipment in this study is being designed with the intention of separating the supporting components of the plastic material from the iron substance so that both materials can be recycled. The strategy used to accomplish the goals of this study is the creation of a heater control system. The findings indicated that for the separation process for iron material portions to be carried out optimally, the temperature needed to melt plastic material was between 177 and 276 oC. Conclusion: This tool's design makes it easier for workers to separate parts with a 93% efficiency, lowering material prices by about Rp. 39,280,000 and handling time with tools by 164,976 seconds. Additionally, the iron material that is separated may be recycled.

Comparison of optimizers for model predictive thermal control of buildings

Considering recent developments in the energy sector, further reduction of electricity cost and flattening of the electric power demand curve are needed. We have focused on an autonomous electric heater control system that can easily be implemented in existing buildings without strict comfort requirements. Examples are winter heating of warehouses and vacation homes, and heat drying of buildings under construction. We have set up a system that typically reduces electricity cost by about 40% on the basis of automatic weather and real time pricing forecasts. The system uses the building as an energy reservoir over periods with high electricity cost. Using a model predictive control system, we compare use of a genetic algorithm, a particle swarm optimization, and a neural network for heater control, all working in a closed loop to reduce the influence of modeling errors. We have simulated the performance of the systems using realistic data and found that all three optimizers give about the same performance, varying only a few percent in efficiency. However, the computational and memory requirements of the neural network are much lower than for the other optimizers, so it is preferable for use with inexpensive microcontrollers. We carried out a full-scale experiment at a residential house and found agreement with simulation results.

Sparse identification modeling and predictive control of wafer temperature in an atomic layer etching reactor

To address the escalating demand and supply constraints for semiconductor devices, manufacturing techniques such as thermal atomic layer etching (ALE) have been utilized, which require robust temperature control systems. Radiative heating with lamps is a promising method for achieving fast and precise temperature control. However, there is limited research in analyzing the dynamic control of radiative lamp heating systems. In this study, a transient, two-dimensional (2-D) radiative heating model in a cross-flow thermal ALE reactor is constructed through Ansys Fluent. A sparse identification (SINDy) modeling approach is applied to build a reduced-order dynamic model for the prediction of system states in the control system. A model predictive controller (MPC) is then developed to bring the wafer surface temperature to the target temperature while preserving the uniformity of the temperature on the wafer surface. Control is implemented by adjusting the powers of three groups of heating lamps independently. Notably, a novel feedback-based time-varying steady-state penalty approach is applied with the MPC in this study, which enables the system to reach the target temperature range within 10 s while maintaining temperature uniformity for 1000 s.

Intelligent control of district heating system based on RDPG

Given the continuous expansion of heating areas in recent years, the design of a precise and dependable district heating system (DHS) has become increasingly crucial. Traditional control decisions are made based on real-time environmental temperature feedback, often leading to uneven heating on the user side and affecting residents' comfort. This paper proposes an intelligent control strategy based on the deep reinforcement learning recurrent deterministic policy gradient (RDPG) algorithm for DHSs. To explore the control performance of the RDPG algorithm on DHS, we have meticulously modeled the pivotal components of the DHSs, namely plate heat exchangers, secondary heating pipe networks, and heat users. Moreover, taking into account the periodic factors in heating regulation, the traditional recurrent neural network (RNN) in the recurrent deterministic policy gradient (RDPG) algorithm has been replaced with the long short-term memory (LSTM) network. The proposed algorithm was trained using actual data from a heat exchange station in Tianjin and compared with reinforcement learning algorithms such as TD3, DPPO, DDPG, and A3C in terms of training rewards, effectiveness, and training stability. The results of the models are evaluated and visualized. Experimental results show that the proposed control method based on the RDPG algorithm, compared to other control schemes, can achieve the highest training reward and the most stable control performance, with an indoor temperature fluctuation range of only 0.1 °C.

Impact of chemical processes on magnetized tangent hyperbolic nanofluid with bio-convection aspects

The study of nanofluids has remarkable uses for researchers because of their fascinating implications in medication delivery, biotechnology, aerodynamic procedures, food privilege, nanotechnology, cool atomic reactors, chemotherapy, next generation film collectors and many more. Currently, the bioconvection technology is measured as a necessary procedure with suppressed compensation in bio-fuel, bio-technology, medical sciences, enzymes, bio-reactors, petroleum chamber machinery, nano-drug delivery, etc. The foremost plan of this exploration is to evaluate the influence of magnetohydrodynamics 3-dimensional flow of tangent hyperbolic nanofluid with gyrotactic motile microorganism through a linear stretching surface. Furthermore, the impressions of mixed convection, Joule heating along with viscous dissipation are as well a part of our research. The analytical expression of tangent hyperbolic liquid flow provides the governing non-linear partial differential equations; a proper transformation is used to transmit this system of equations in ordinary differential equations. To find out the numerical solution, the dimensionless equations are determined by means of BVP4C technique in MATLAB. The influences of miscellaneous substantial parameters on temperature, concentration along with motile microorganisms are accomplished by means of graphs. It can be observed that the heat transfer profile of tangent hyperbolic nanoliquid escalates by increasing the approximations of Eckert number, heat sink/source and power law index factor. A conflicting behavior is observed for Brownian motion parameter, stretching rate parameter along with Schmidt number in concentration profile. Furthermore, Peclet number and bioconvection Lewis number have decreasing impacts for microorganism profile. These nano fluid models with gyrotactic microorganisms are implemented in biodiesel fabrication, heat control systems, food factories, biomechanics, biomedical engineering, thermal exchangers, waste water treatment plant aero dynamical systems, and fundamental sustainable energy resources.

A novel deep-learning framework for short-term prediction of cooling load in public buildings

Optimal control of heating, ventilation, and air conditioning (HVAC) systems, along with demand-side management, are both cost-effective methods in the process of energy conservation and carbon reduction. The successful implementation of these initiatives largely hinges on accurate cooling load predictions. Due to the complex nonlinear and dynamic time-varying nature of demand loads, however, it is a formidable challenge to accurately predict the cooling load. To address these issues, a novel deep learning-based prediction framework, aTCN-LSTM, is proposed. First, a gate-controlled multi-head temporal convolutional network is designed to capture the inherent nonlinear and local temporal features from the time series of cooling loads. Second, a sparse probabilistic self-attention mechanism is integrated with a bidirectional long short-term memory (BiLSTM) network to extract the long-term dependencies within the cooling load sequences. Finally, through integration with the proposed two components, the framework is developed and validated through a 14-month real cooling load forecasting problem for a 51-story hotel building in Guangzhou, China. Experiments and comparison studies demonstrate the effectiveness and superiority of the proposed method. The mean absolute percentage error of the proposed method's 1-step, 6-step, and 12-step prediction results is reduced by 27.48%, 14.05%, and 13.38%, respectively, compared with the state-of-the-art baseline model. Consequently, it stands poised to serve as an effective guide for HVAC chiller scheduling and demand management initiatives.

MCU-Based PID Temperature Control System for Linear Heating and Cooling

In this study, we have designed and developed a PID temperature controller with the core control unit based on the STC12 microcontroller, along with AD conversion chips and LM2596 power modules. This system effectively maintains temperature stability within a set range, with a steady-state error of less than 0.1°C. Moreover, it allows for precise and near-linear temperature ramping from room temperature to 400°C, and subsequent linear fitting of the heating curve consistently yielded coefficient of determination exceeding 0.999. This innovative approach represents a heating method suitable for semiconductor sensors and holds significant promise for application in industrial settings.

Approach to Modernizing Residential-Dominated District Heating Systems to Enhance Their Flexibility, Energy Efficiency, and Environmental Friendliness

The need to modernize existing district heating systems is due to increased requirements for their flexibility, energy efficiency, and environmental friendliness. The technical policy on district heating pursued in different countries centers on the listed goals and takes account of historical, climatic, and regional features of the resource, technology, and economic availability of various thermal energy sources. This study aims to analyze methods designed to improve the flexibility, energy efficiency, and environmental friendliness of district heating systems. The focus of the study is district heating system, which provides heating and hot water supply to consumers and consists of various types of thermal energy sources. The work shows the possibility for the heating system to transition from the third generation to the fourth one, which differ in their level of intellectualization. The establishment of an intelligent control system will ensure the interaction of various heat sources, but this is a separate strand of research. In this study, a model and a methodology were developed to optimize the structure of thermal energy sources and their operating conditions when covering the heat load curve of a territory with a predominance of household consumers. Gas-reciprocating and gas-turbine cogeneration plants are considered as the main thermal energy sources, whose efficiency is boosted through their joint operation with electric boilers, thermal energy storage systems, low-grade heat sources, and absorption chillers. The primary emphasis of the study is on the assessment of the environmental benefit to be gained by using cogeneration plants as a factor of enhancing the investment appeal of the district heating systems. The findings suggest that the transition of district heating systems to the next generation is impossible without changing the institutional environment, strengthening the role of active consumers, and introducing intelligent control for district heating systems.

Estimating perceived indoor air quality and environmental satisfaction using a camera

Although indoor environmental monitoring data are increasingly available, they often fail to accurately capture personal environmental perception, comfort, and satisfaction. This disconnection has led to a growing interest in using real-time feedback from building users to improve the control of heating, ventilation, and air-conditioning systems and reduce energy waste. However, traditional questionnaire-based surveys only provide a snapshot at the time of investigation and suffer from a variety of bias sources. To address this, this paper proposes a novel camera-based method for estimating perceived indoor air quality and environmental satisfaction by analysing facial expressions. We conducted indoor air pollution exposure experiments on 24 healthy adults in twin environmental chambers. Their changes in facial expressions were compared between different exposure conditions and the potential correlation between facial expressions and perceived air quality, environmental satisfaction, and self-reported sick building syndrome symptoms was explored. Preliminary results indicate that a common RGB camera can repeatably detect facial expressions from the same subject. Also, results from a subset of five subjects suggest moderate differences in concentration, calmness, contemplation, and tiredness expressions between the two air quality conditions.

Adaptive model predictive control of a heat pump-assisted solar water heating system

Full electrification of building energy systems makes that the electricity-driven heat pump and solar heat become the most promising heating sources for hot water production in the future. The heat pump assisted solar water heater will be a good solution to jointly use these two heating sources. The system has large potential in demand response because of large capacity thermal storage tank which requires reasonable optimal control. In this context, this paper presents an adaptive model predictive control (AMPC) to achieve demand response with adaptive boundary and linear time-varying heat pump efficiency. Using adaptive parameters, the controller improves the adaptability and achieves optimal control under various disturbance conditions. The results demonstrated the AMPC realize 20% cost saving and 12% energy saving compared with PID, which are 7% reduction in costs and a 3% decrease in energy consumption compared to conventional MPC methods. Furthermore, a detailed analysis research was conducted on the controller parameters and disturbances, considering equipment parameters, electricity pricing models, weather conditions, and load types. Notably, the MPC exhibited even greater performance improvements in scenarios involving real-time pricing, concentrated loads, and low heat pump capacity.