Temperature Control Model Research Articles

Modeling and Regulation of Dynamic Temperature for Layer Houses Under Combined Positive- and Negative-Pressure Ventilation.

The environmental control of layer houses with multi-tiered cage systems is influenced by factors such as the structure of the henhouses and the heat dissipation of the flock, leading to low precision and large fluctuations in temperature control. Based on a new combined positive- and negative-pressure ventilation (CPNPV) mode, a dynamic temperature model is constructed. Additionally, a temperature control method for a layer house is designed using a variable universe fuzzy PID control algorithm (VFPID). First, based on the principles of energy and mass balance, and by decoupling the relationship between positive- and negative-pressure ventilation volumes, a dynamic temperature model for layer houses under CPNPV was established. Then, the PID parameters and the proportional relationship between positive- and negative-pressure ventilation were optimized through fuzzy rules, and a proportional exponential function was introduced to adjust the scaling of the universe, enabling fine-tuned control. Finally, a temperature control model for the layer house was built using Simulink. The results show that the coefficients of determination (R2) of the constructed dynamic temperature models are between 0.79 and 0.88, respectively, indicating high accuracy. The designed VFPID method outperformed traditional on-off control and improved control precision by 20-23.53% and 10.34-22.22% compared with PID control and fuzzy PID(FPID) control methods, respectively. This study provides new insights for the development of environmental control equipment and precise environmental regulation of layer houses.

Modeling, simulations, and Simulink developments in the analysis of optimal control of temperature and pH in a batch ethanol fermentation process

Transfer of laboratory-scale experiments to production-scale ethanol fermentation is time-consuming and involves expensive prototype systems from complex experimental designs that determine optimal operating conditions for minimal substrate and product inhibitions. The study developed and validated a Simulink-based model for optimal pH and temperature control using fuzzy logic and PID controllers respectively and taking advantage of 2D and 1D substrate and product inhibition models from which suitable ethanol fermentation reaction rates models were selected. Temperature and pH levels and substrate, product, and biomass concentrations were measured. Selected inhibition models were linear-product, linear substrate-sudden stop product, and linear substrate for cassava, maize, and sorghum, respectively. Fuzzy logic controller ascertained optimal flow rate of acid and base as 0.000196 ml/s and 0.000204 ml/s, respectively, and pH error and rate of pH error as 0.00334 and 0.00368, respectively. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1905; maize: F critical 0.9704, F calculated = 0.2149; sorghum: F critical = 0.9704, F calculated = 0.2488). PID logic controller showed model curves and experimental curves with good fit. F-test two-sample for variances showed no significant difference between model and experimental curves (cassava: F critical = 0.9704, F calculated = 0.1288; maize: F critical = 0.9704, F calculated = 0.2083; sorghum: F critical = 0.9704, F calculated = 0.2016). The study provided an improved approach as solution for optimal pH and temperature conditions in order to mitigate substrate and product inhibitions during ethanol fermentation. It illustrated that the application of artificial intelligence-based controllers provides satisfactory outcomes that are desirable for implementation in the industrial space.Graphical

Numerical simulation and experimental verification of dynamic cooling and temperature control of the optical cavity in a CO2 isotope analyzer

Accurate measurement of CO2 generated from different emission sources is the basis for controlling CO2 emissions and mitigating global warming. With advantages of high accuracy and rapid response, isotope analyzers using spectroscopic techniques, particularly those using cavity ring-down spectroscopy, have been widely used for CO2 emissions monitoring. However, these analyzers often face challenges such as spectral interference from impurity gases and unstable temperature control within the optical cavity in isotope analyzers. To address these challenges, this paper proposes a low-temperature optical cavity scheme utilizing a split Stirling-type pulse tube cryocooler. Combining thermal boundary conditions with particle swarm optimization, a dynamic cooling and temperature control model of the optical cavity was established in MATLAB. The model subsequently analyzed the cooling process, temperature control, and stability of the optical cavity. After 120 h, the simulations showed that the central part of the optical cavity had cooled to 126.4 K with a heat leakage of 8.90 W, in agreement with the experimental results that stabilized at 126.8 K. The temperature fluctuations remained within 0.1 K/h while controlling an overall temperature of 170 K with a temperature difference of ± 5 K. Changes in the electrical input power of cryocooler had no influence on the temperature distribution. This advancement not only lowers the operating temperature of the optical cavity to minimize spectral interference but also enhances the precision and efficiency of temperature control.

Development path of digital cultural industry by integrating proportional integral and differential algorithm from the perspective of cultural innovation

The digital culture business benefits from quick technical updates, digital manufacturing, communication networks, and customized consumption, which promotes new supply and consumption. The innovation and development of the digital cultural industry cannot be separated from the physical industry. The efficient production of the physical industry can ensure the innovative development of digital cultural industry. Therefore, to improve the production efficiency of physical industry, this research proposes a temperature control model combining whale optimization algorithm and fuzzy proportional integral differential algorithm based on the traditional temperature control in ceramic firing and wine making process. Firstly, fuzzy control theory is used to improve the proportional integral differential algorithm, and a fuzzy proportional integral differential model with adaptive adjustment function is established. On this basis, to quickly confirm the optimal parameters of the proportional integral differential model, the whale optimization algorithm is introduced to further optimize its weight factors. It was verified that the temperature control adjustment model combining whale optimization algorithm and fuzzy proportional integral differential model was 2.77 s, which was shortened by 1.11 s, 2.37 s and 3.13 s, respectively than the other three models. Therefore, the constructed temperature control model can effectively realize high-precision intelligent temperature control in industrial production, laying the cornerstone for promoting the innovative development of digital culture industry. The proposed model can improve the production efficiency of physical industries, promote digital transformation, and optimize the innovative development of digital cultural industries.

Nonlinear sparse variational Bayesian learning based model predictive control with application to PEMFC temperature control

The accuracy of the underlying model predictions is crucial for the success of model predictive control (MPC) applications. If the model is unable to accurately analyze the dynamics of the controlled system, the performance and stability guarantees provided by MPC may not be achieved. Learning-based MPC can learn models from data, improving the applicability and reliability of MPC. This study develops a nonlinear sparse variational Bayesian learning based MPC (NSVB-MPC) for nonlinear systems, where the model is learned by the developed NSVB method. Variational inference is used by NSVB-MPC to assess the predictive accuracy and make the necessary corrections to quantify system uncertainty. The suggested approach ensures input-to-state stability (ISS) and the feasibility of recursive constraints in accordance with the concept of an invariant terminal region. Finally, a PEMFC temperature control model experiment confirms the effectiveness of the NSVB-MPC method.

Grouping Neural Network-Based Smith PID Temperature Controller for Multi-Channel Interaction System

The thermal vacuum test (TVT) is an important verification process in the development of spacecraft and load. There are often multiple temperature points on the device under test (DUT) that require control. The interaction among multiple channels poses a challenge for temperature control in the TVT. To solve this problem, a multi-channel Smith proportional–integral–derivative (PID) controller based on a grouping neural network (Grouping-NN) is proposed. Firstly, the mathematical derivation for a typical multi-channel temperature control model of the TVT is carried out. Then, the multi-channel interaction system is identified using a Grouping-NN to predict the output temperature of each channel by grouping the hidden layer neurons according to the number of channels. Finally, two Grouping-NNs are utilized to update the Smith predictor, and the time-delay error is fed back to the PID controller, which is used to optimize the control effect of the multi-channel interaction system under high time delay. The proposal is compared with the traditional PID controller and Smith predictor-based PID controller through simulation. The simulation results show that the proposed method has better suppression of overshooting. In addition, the algorithm is verified by controlling the temperature of six channels in a practical thermal vacuum test.

Experimental and numerical study on the model of hybrid fiber phase change concrete frozen shaft wall.

In this study, phase change materials (PCMs) were innovatively incorporated into hybrid fiber concrete. The properties of PCMs, which absorb and release heat during phase transitions, enable the concrete to actively respond to complex and varying temperature environments. This integration reduces the internal temperature differentials within the concrete, thereby preventing temperature-induced cracks in deep wellbore structures. Through the temperature control model test of the frozen shaft wall, it can be seen that the hybrid fiber phase change concrete (HFPCC) significantly reduces the internal temperature difference, and the maximum temperature difference along the radial direction is 35.84% lower than that of benchmark concrete (BC). The numerical simulation results indicate that a moderate phase transition temperature should be selected in engineering. The phase change temperature should not be close to the ambient temperature and peak temperature. The peak temperature can be reduced by 9.32% and the maximum radial temperature difference can be reduced by 30.89% by selecting an appropriate phase change temperature. The peak temperature and radial maximum temperature difference are both proportional to the latent heat of phase change. The temperature control performance of phase change concrete can be further improved by increasing the latent heat of phase change materials.

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

A PSO-optimized novel PID neural network model for temperature control of jacketed CSTR: design, simulation, and a comparative study

Hybrid Model for Metal Temperature Control during Hot Dip Galvanizing of Steel Strip

The paper proposes a hybrid model for predictive control under step disturbances that lead to a sharp jump in the state of the process. Similar changes occur when controlling the temperature of the steel strip on continuous hot-dip galvanizing units. Periodic changes in strip gauge or strip speed result in abrupt changes in the temperature of the steel at the outlet of the annealing furnace. During such periods deviation control is difficult requiring introduction of tolerances that limit productivity and leading to excessive heating of the metal. The paper shows that the existing proposals for controlling the temperature of the steel strip are not effective enough with a sharp change in the state of the process. The reasons for this are unknown disturbances operating in a wide frequency range and having low-frequency and trend components, as well as many influencing factors. It is shown that the problems of representativeness of the initial accumulated data make it difficult to create complex empirical models, and the level of uncertainty of the processes in the furnace makes it difficult to create complex interpretable models. The proposed hybrid model involves combining two types of simplified interpretable process models, as well as an empirical model based on an artificial neural network. The errors of the interpreted models are shown to be effectively predicted by a neural network in the presence of an additional signal from an observer of unknown disturbances. Computational experiments carried out on the data of one of the units of MMK PJSC in Russia showed that the hybrid model provides high accuracy of steel strip temperature prediction during technological disturbances and does not require frequent reconfiguration.

Computational fluid dynamics modeling of a wafer etch temperature control system

Next-generation etching processes for semiconductor manufacturing exploit the potential of a variety of operating conditions, including cryogenic conditions at which high etch rates of silicon and very low etch rates of the photoresist are achieved. Thus, tight control of wafer temperature must be maintained. However, large and fast changes in the operating conditions make the wafer temperature control very challenging to be performed using typical etch cooling systems. The selection and evaluation of control tunings, material, and operating costs must be considered for next-generation etching processes under different operating strategies. These evaluations can be performed using digital twin environments (which we define in this paper to be a model that captures the major characteristics expected of a typical industrial process). Motivated by this, this project discusses the development of a computational fluid dynamics (CFD) model of a wafer temperature control (WTC) system that we will refer to as a “digital twin” due to its ability to capture major characteristics of typical wafer temperature control processes. The steps to develop the digital twin using the fluid simulation software ANSYS Fluent are described. Mesh and time independence tests are performed with a subsequent benchmark of the proposed ANSYS model with etch cooling system responses that meet expectations of a typical industrial cooling system. In addition, to quickly test different operating strategies, we propose a reduced-order model in Python based on ANSYS simulation data that is much faster to simulate than the ANSYS model itself. The reduced-order model captures the major features of the WTC system demonstrated in the CFD simulation results. Once the operating strategy is selected, this could be implemented in the digital twin using ANSYS to view flow and temperature profiles in depth.

An online BOF terminal temperature control model based on big data learning

An online BOF terminal temperature control model based on big data learning

A mathematical model of catalyst combination design and temperature control in the preparation of C4 olefins through ethanol coupling.

The preparation of C4 olefins through ethanol catalytic coupling is a crucial area of study. According to the experimental data obtained by a chemical laboratory for different catalysts at different temperatures, three mathematical models were developed to provide insights into the relationships among ethanol conversion rate, C4 olefins selectivity, yield, catalyst combination, and temperature. The first model is a nonlinear fitting function that analyses the relationships among ethanol conversion rate, C4 olefins selectivity, and temperature under varying catalyst combinations. Two-factor analysis of variance was employed to determine the influence of catalyst combinations and temperatures on ethanol conversion rate and C4 olefins selectivity. The second model is a multivariate nonlinear regression model that describes the relationships among the yield of C4 olefins, catalyst combination, and temperature. Finally, an optimization model was derived based on the experimental conditions; it provides a solution for the selection of the optimal catalyst combinations and temperatures to achieve the maximum yield of C4 olefins. This work has significant implications for the field of chemistry and the production of C4 olefins.

Predictive control of microwave hot-air coupled drying model based on GWO-BP neural network

A GB-MPC control algorithm (GWO-BP-MPC) was proposed to solve the problem of precise temperature control of fruit and vegetable coupling drying devices. Firstly, the BP (Back Propagation) neural network was improved using the Grey Wolf Optimizer (GWO) algorithm to increase the relevance and accuracy of the prediction model. By means of an improved neural network, we developed a high-accuracy predictive model for temperature control of drying units. Secondly, the projection conjugate gradient method was proposed for nonlinear optimization of the control system to improve the solving speed and accuracy of the optimal solution. The GB-MPC control algorithm was compared with the PID controller. The experimental results shown that the convergence speed of GB-MPC control was faster, the time took to reach a steady state in a single stage was shortened by 47 seconds compared with PID control. In the control process, the temperature change range of the GB-MPC control algorithm was smaller and there was no overshoot problem, which gave a better control effect than PID.

Experiments and microsimulation of high-pressure single-cell PEM electrolyzer

Direct hydrogen production at high pressure by the proton exchange membrane (PEM) can improve the economy of the entire hydrogen-chain. A multi-physical model of a high pressure single-cell electrolyzer is established and the heat transfer, mass transfer, and electrochemical processes are described. The water permeation flow rate is simulated accurately using experimental data obtained under 0-700 bar pressure difference. The double layer effect and mass transfer lag effect are considered in the dynamic model in terms of different time constants. Polarization curves exhibit good consistency at different temperatures and high cathode pressure, and the average the sum of squares due to error (SSE) is less than 0.01. Different film thicknesses correspond to different safe operating zones (hydrogen concentration in oxygen below 2%). A membrane thickness of 250μm allows the cathode pressure to increase to approximately 120 bar, and the minimum operating current density should not be less than 0.2 A/cm2. Increasing the cathode pressure to 200 bar can reduce the water content on the hydrogen side by more than 19.5%. Finally, the temperature control model of circulating water in the electrolysis cell is calibrated and verified by experiments, and the steady-state error is less than 2.5%.

MATHEMATICAL MODELING OF THE MPPT-CONTROLLER FOR RENEWABLE ENERGY SOURCES ON MOSFET TRANSISTORS

This article discusses field tests and practical implementation of a low-voltage LED lighting system with renewable energy sources in an autonomous facility and the development of a model for optimal temperature control in an autonomous facility. The method of complex management of the power supply system using the Smart controller of the energy supply of the autonomous object is also considered. To implement the mathematical model, the Simulink and SimPowerSystems software for visual modeling included in the MatLab package was used. The use of this software allows you to simulate complex electrical systems. The advantage of using this program is the combination of methods of structural and simulation modeling, and this approach provides the flexibility of the system for the model being developed, greatly simplifies the model, and therefore increases the speed of calculations. When developing indoor temperature control systems, a number of problems arise, such as determining the time required the temperature to rise to provide the set values to the required moment, choosing the temperature control algorithm and determining the parameters of the selected algorithm. The task of choosing the control law and the type of controller is to choose such a controller, which at minimum cost and maximum reliability would provide the specified quality of regulation. In this article, the choice was made on the PID regulator, since with a jump-like change of the regulated value, the PID regulator at the initial moment of time has an infinitely large influence on the object of regulation. Then the magnitude of the influence drops sharply to the value determined by the proportional component, after which the integral component of the regulator begins to exert its influence constantly. The transient process in PID controllers has minimal deviations in amplitude and time compared to P and PI controllers (i.e., the highest speed).

Control of Large-Scale Delayed Networks: DDEs, DDFs and PIEs

Delay-Differential Equations (DDEs) are the most common representation for systems with delay. However, the DDE representation has limitations. In network models with delay, the delayed channels are typically low-dimensional and accounting for this heterogeneity is challenging in the DDE framework. In addition, DDEs cannot be used to model difference equations. In this paper, we examine alternative representations for networked systems with delay and provide formulae for conversion between representations. First, we examine the Differential-Difference (DDF) formulation which allows us to represent the low-dimensional nature of delayed information. Next, we consider the coupled ODE-PDE framework and extend this to the recently developed Partial-Integral Equation (PIE) representation. The PIE framework has the advantage that it allows the H∞-optimal estimation and control problems to be solved efficiently using the recently developed software package PIETOOLS. In each case, we consider a very general class of networks, specifically accounting for four sources of delay - state delay, input delay, output delay, and process delay. Finally, we use a scalable network model of temperature control to show that the use of the DDF/PIE formulation allows for optimal control of a network with 40 users, 80 states, 40 delays, 40 inputs, and 40 disturbances.

Recurrent Neural Network-Based Temperature Control System Weight Pruning Based on Nonlinear Reconstruction Error

Recurrent Neural Networks (RNNs) have been widely applied in various fields. However, in real-world application, because most devices like mobile phones are limited to the storage capacity when processing real-time information, an over-parameterized model always slows down the system speed and is not suitable to be employed. In our proposed temperature control system, the RNN-based control model processes the real-time temperature signals. It is necessary to compress the trained model with acceptable loss of control performance for further implementation in the actual controller when the system resource is limited. Inspired by the layer-wise neuron pruning method, in this paper, we apply the nonlinear reconstruction error (NRE) guided layer-wise weight pruning method on the RNN-based temperature control system. The control system is established based on MATLAB/Simulink. In order to compress the model size to save the memory capacity of temperature controller devices, we first prove the validity of the proposed reference-model (ref-model) guided RNN model for real-time online data processing on an actual temperature object; relative experiments are implemented based on a digital signal processor. On this basis, we then verified the NRE guided layer-wise weight pruning method on the well-trained temperature control model. Compared with the classical pruning method, experiment results indicate that the pruned control model based on NRE guided layer-wise weight pruning can effectively achieve the high accuracy at targeted sparsity of the network.

Investigation of optimal operating temperature for the PEMFC and its tracking control for energy saving in vehicle applications

The operating temperature, as one of the key factors influencing the system efficiency is very critical for the proton exchange membrane fuel cell (PEMFC). However, the specific relationship between the operating temperature of PEMFC and its efficiency remains unclear, which further challenge the high-efficiency operation of the PEMFC in the fuel cell vehicle (FCV). This paper aims to decouple the operating temperature from the complicated driving conditions of the FCV, and find the mapping relationship between the operating temperature and PEMFC output power, hence to improve the efficiency by the optimal control of PEMFC system. A temperature control model of the PEMFC cooling system is established and enhanced by a series of experiments under different power levels against different temperature. The mapping relationship between the optimal operating temperature and the PEMFC power is obtained by the experiments. Then, a predictive control with the function of tracking the optimal operating temperature is developed and verified under NEDC and CHTC-HT driving cycles. Simulation results show that compared with constant temperature control and the traditional rule-based temperature control, under the optimal temperature tracking control, the PEMFC efficiency is improved by 15% and 18.2%, 25.6% and 28.7% respectively in the two driving cycles.

Model of Temperature Control by Electrically Stimulating Action Parameters

Model of Temperature Control by Electrically Stimulating Action Parameters

Use of the multi-factor regression model for temperature control in structurally isolated rooms of the railway stations

The article is devoted to the analysis and construction of a multivariate regression model. The mode of operation of generalized heat points is considered. The positive and negative aspects of using heat points to maintain the temperature at the desired level have been identified. The analysis of the multivariate model is carried out. The input data for the generated model has been defined. The factors that affect the temperature in structurally isolated rooms have been identified. A multifactorial regression model of temperature control in heated rooms has been built, with the help of which it is possible to determine how much heat needs to be supplied to a particular structurally isolated room, which will allow more efficient use of resources to maintain the temperature at the required level.