Adaptive Temperature Control Research Articles

Adaptive control for refrigeration via online identification

Vapor compression refrigeration systems (VCRS) occupy a crucial position in modern society and in the field of thermal sciences. However, the operation of VCRS is subjected to both external disturbances (dynamic-changing environment) and inherent system characteristics (coupling or nonlinear features), leading to issues like reduced refrigeration efficiency and significant fluctuations in cooling capacity. To address these challenges and enhance the adaptability of VCRS in dynamically changing environments, this study establishes a dynamic simulation model for VCRS based on the Switched Moving-Boundary method. The impact of external environmental disturbances on refrigeration performance is investigated, and continuous online identification methods are employed to elucidate its internal coupling characteristics and nonlinear features. The adaptive temperature control method is introduced, benefiting from the developed recursive least squares method with a forgetting factor for online identification, achieving precise model identification which facilities the real-time parameters tuning of adaptive controller. The results indicate that the hybrid paradigm of online identification and adaptive control algorithm not only effectively handles various disturbances but also reduces overshoot and IAE by 2–3 orders of magnitude compared to traditional PID controllers. Adaptive PID control maintains overshoot in the 10–4 order of magnitude and IAE in the 10–5 order of magnitude.

The integrated performance of adaptive temperature control and temperature measurement of PTC materials with high thermal cycle repeatability

Abstract As the temperature increases, the resistance of the positive temperature coefficient (PTC) material becomes larger. Using this material can reduce the complexity of the temperature control system. In this paper, a new PTC material is developed by means of melt blending, which still has operable PTC characteristics after 60 high and low temperature cycles. The data points collected in multiple thermal cycles were fitted to obtain the relationship between the sample’s temperature and resistance. In addition, the integrated performance of adaptive temperature control and temperature measurement of PTC materials is proposed for the first time. Based on the prepared samples, the temperature control characteristics of PTC materials on the controlled object under different environmental conditions are studied experimentally, and the temperature value converted from the resistance value is compared with the temperature value measured by the platinum resistance. The results show that the prepared sample has good temperature control performance, and the material has an operable temperature measurement function in the PTC effect range.

Study on the influence of thermal properties of paraffin on flexible PTC materials

Abstract This paper experimentally studied paraffin’s influence on flexible PTC materials’ temperature resistance and flexibility. Their resistance-temperature characteristics are notably influenced by PA’s phase transition range thermal properties. Specifically, the Curie temperature is intricately linked and determined by the PA’s phase transition temperature. PA’s phase state and proportion in the composite directly impact PTC material flexibility significantly. Specifically, a lower PA phase transition temperature results in better flexibility of PTC materials at room temperature. Additionally, a lower PA content minimizes interference with the overall flexible mesh structure, further enhancing material flexibility. The PTC of this series of formulations showed good adaptive temperature control ability. The thermal control performance can be customized by adjusting the PA.

High-efficiency adaptive temperature control for thermoelectric system based on the OBPPID strategy

Thermoelectric cooler (TEC) is widely used for temperature control in optoelectronics, machinery, biomedicine, and other fields. However, the unstable temperature control over wide ranges and different gradients restricts the efficiency of TEC. To highlight the neural network’s effectiveness in controlling TEC systems, the optimized BPPID (OBPPID) strategy is proposed. In the OBPPID strategy, the factors s̃ and G are decided through formula derivation and the particle swarm optimization (PSO) algorithm, respectively contributing to the backward adjustment and forward propagation of BPPID. The OBPPID solves three issues that the BPPID faces: gradient vanishing of network weights, over-reliance of initial network weights and range constraint of control parameters. The OBPPID and BPPID have been simulated 1000 times to evaluate control performance. The results show that, with random initial network weights, the OBPPID strategy reduces the cost variability of the BPPID strategy from 92 to 17, achieving an 81.52% reduction. Physical tests have confirmed that in the temperature control over wide range and the temperature gradients of 5°C, 20°C, and 80°C, the OBPPID strategy can adaptively adjust the control parameters and provide higher efficiency than PID, BPPID and fuzzy PID strategies for TEC system.

Real-time temperature control in rubber extrusion lines: a neural network approach

In rubber extrusion, precise temperature control is critical due to the process’s sensitivity to fluctuating parameters like compound behavior and batch-specific material variations. Rapid adjustments to temperature deviations are essential to ensure stable throughput and extrudate surface integrity. Based on our previous research, which initiated the development of a feedforward neural network (FNN) without real-world empirical application, we now present a real-time control system using artificial neural networks (ANNs) for dynamic temperature regulation. The underlying FNN was trained on a dataset comprising different ethylene propylene diene monomer (EPDM) rubber compounds, totaling 14,923 measurement points for each temperature value. After training, the FNN achieves remarkable precision, evidenced by a mean absolute percentage error (MAPE) of 0.68% and a mean squared error (MSE) of 0.63°C2 in predicting temperature variations. Its integration into the control system enables real-time responsiveness, allowing for adjustments to temperature deviations within an average timeframe of 68 ms. A key advantage over proportional-integral-derivative (PID) controllers is the ability to continuously learn and adjust to complex, non-linear, and batch-specific process dynamics. This adaptability results in enhanced process stability, as evidenced by inline manufacturing validation. Our paper presents the first ANN-based rubber extrusion control, demonstrating how machine learning techniques can be effectively leveraged for real-time, adaptive temperature control. Beyond rubber extrusion, this strategy has potential applications in various polymer processing and other industries requiring precise temperature control. Future trends may involve the integration of online learning techniques and the expansion of interconnected manufacturing processes.

Application of an improved simulated annealing algorithm in satellite network routing

Abstract Satellite communication is integral to modern communication systems, yet navigating the intricacies of routing in networks characterized by high loads and dynamic evolution poses a significant challenge. This research is dedicated to enhancing satellite communication routing algorithms, with the goal of bolstering network efficiency and amplifying the success rate of business transmissions. Our approach introduces an improved simulated annealing algorithm incorporating adaptive temperature control and innovative path neighborhood exploration strategies. This novel algorithm is adept at adjusting to ever-shifting network topologies and meeting various business requirements. Our simulation results reveal that this refined algorithm outperforms traditional annealing methods in several critical aspects. It elevates the success rate of business transmissions, reduces the average number of forwardings by 6.546% across various load scenarios, and diminishes system response time by 15.05%, thereby marking a substantial improvement in network performance.

Adaptive temperature control of a reverse flow process by using reinforcement learning approach

This work focuses on the design of an optimal adaptive control system for temperature regulation in a catalytic flow reversal reactor (CFRR), utilizing a reinforcement learning (RL) approach. First, a policy iteration algorithm is introduced to learn the optimal solution of the associated linear-quadratic control problem online. It should be mentioned that this approach is not reliant on the internal dynamics of the CFRR system, which is a complex process and is most effectively modeled using Partial Differential Equations (PDEs). The convergence of the iteration algorithm is established, assuming the initial policy is stabilizing. Additionally, a second algorithm is presented to enhance the implementability of the reinforcement learning algorithm from a practical perspective. Numerical simulations are carried out to illustrate the efficacy of the proposed algorithm.

Positive temperature coefficient material based on silicone rubber/ paraffin/ graphite/ carbon nanotubes for wearable thermal management devices

PTC materials provide substantial advantages such as adaptive temperature control performance and excellent safety. However, their use in wearable devices has been limited due to the essential requirements for flexibility and stability. This study prepared a new type of flexible PTC material, with a PTC intensity of 6.3 and a resistivity of only 400 Ω·cm in the low-temperature region. The flexibility of the material is evidenced by its 2 mm bending diameter and its ability to maintain structural integrity even after 100 bends. Furthermore, experimental evidence proves that PTC material has excellent thermal stability and can withstand extreme temperatures ranging from −20 °C to 120 °C. In the thermal control experiment, the PTC material maintains a temperature of approximately 25 °C to accommodate the requirements of the human body in a cold environment. When compared to the conventional PI heating sheet, the newly prepared PTC material exhibits advantages such as rapid response, adaptive temperature control performance, and safety. Consequently, it presents a broad application prospect in the field of wearable heating devices.

Adaptive state-of-health temperature sensitivity characteristics for durability improvement of PEM fuel cells

To obtain the temperature sensitivity characteristics of the proton exchange membrane fuel cell under different degradation levels, accelerated durability tests and temperature sensitivity tests of the fuel cell are first performed, and the temperature characteristics under different degradation states are analyzed by electrochemical impedance spectroscopy and polarization curve. Besides, an adaptive state-of-health transient thermal model of fuel cells is developed to investigate the effect of operating temperature on the internal gas concentration and hydrothermal distribution characteristics of the fuel cell from a mechanistic perspective. The experimental results of temperature sensitivity validate that the adaptive state-of-health transient thermal model can effectively monitor the steady and transient performance of the fuel cell. Subsequently, a mathematical model is proposed to describe the relationship among the load current, state of health, and optimal temperature using the temperature sensitivity test data and the transient thermal model of the adaptive state of health, which provides important insights into the optimal temperature range tailored to the current state of health of fuel cells to ensure efficient and stable operation of fuel cells all the time and contributes to the fine design of an optimal degradation adaptive temperature control strategy.

Adaptive backstepping control for uncertain semiconductor refrigeration systems considering even-order input

Temperature control technique has received much attention in the field of semiconductor thermal management. This paper presents a novel adaptive backstepping-based temperature control strategy for semiconductor refrigeration systems considering unmodeled dynamics. The inherent non-affine problem caused by the even-order input signal is solved by combining a novel smooth function into the command filtered backstepping method. And then, the adaptive observer is also employed to improve the system performance under unmodeled dynamic deviations. The stability of the closed-loop system is analyzed based on the Lyapunov theory, and simulation experiments are given to verify our control method. The simulation results show that the method can realize the accurate control of temperature under unmodeled dynamics. Under the disturbance of ambient temperature changes and model errors, the steady state error of the system remains below 0.1 °C. In comparison with the traditional control methods, our strategy has a stronger ability to suppress the uncertainty effects.

Design of MFAC greenhouse temperature controller based on LESO with disturbance estimation and fuzzy controller with parameter tuning

The proper temperature is essential for the growth and development of greenhouse crops. Therefore, it is necessary to determine an appropriate greenhouse temperature automatic control strategy. In this paper, a model-free adaptive greenhouse temperature control strategy is proposed by combining a linear extended state observer and a fuzzy controller. Specifically, the linear extended state observer is introduced to estimate model approximation error generated by pseudo gradient estimation. The fuzzy controller is used to correct the system parameter ρ online. The simulation results show that when there is no disturbance, compared with the typical model-free adaptive scheme, the MAE and RMSE of the greenhouse temperature control scheme proposed in this paper are reduced by 57.55% and 5.40% respectively. In the presence of custom disturbances, the proposed control scheme still has a faster response speed, stronger robustness, and higher control accuracy. And the proposed method can reduce the frequent action of the actuator, thus effectively reducing the energy cost.

Research on establishment of digital-twin system for intelligent control of cutting tools sintering process driven by data-model combination

In the manufacturing process of tungsten cemented carbide cutting tools, the changes in temperature, pressure and vacuum during the sintering process of the cutting tools can have a significant impact on the quality of the cutting tools. This effect is particularly significant in the vacuum sintering stage, so this paper focuses on cutting tools sintering in the vacuum stage. The cutting tools sintering process is carried out in a closed space in a sintering furnace with a complex internal structure. Therefore, it is extremely difficult to accurately predict the heating condition of the cutting tools in the furnace and monitor the sintering quality of the cutting tools. In this study, a digital twin system for the sintering process of cemented carbide cutting tools is proposed, which combines monitoring rolling data analysis and sintering quality prediction model. Firstly, the radiative thermodynamic model of cutting tools sintering temperature was established in this system. The model predicts the time-varying state of the heat distribution at any location in the furnace during the vacuum phase. Subsequently, based on the convolutional neural network, the mapping relationship between the sintering parameters and the grain size in the vacuum stage is constructed, and a dynamic evaluation model of cutting tools sintering quality is proposed. The model enables real-time prediction of sintering quality in the vacuum stage. The system realizes the prediction of the time-varying state of the sintering quality of the cutting tools. The digital twin system realizes the adaptive temperature control of the vacuum stage of cutting tools sintering based on the fuzzy control algorithm. The application of this system has increased the yield rate of cemented carbide cutting tools manufacturing to 98.9%. This breaks through the problem of timing modulation of cutting tools sintering quality in the vacuum stage of cutting tools sintering.

Experimental investigation on the open cathode air-cooled proton exchange membrane fuel cells: Optimum operating parameters and control strategies

Operating parameters and control strategies are crucial for the performance of open cathode air-cooled proton exchange membrane fuel cells. In present study, effects of hydrogen pressure, anode purging ratio and stack temperature on an open cathode air-cooled PEMFC with 40 single cells are experimentally explored. Comprehensive evaluations of operating parameters and control strategies are adopted from multiple aspects including output performance, stability, hydrogen consumption, and system efficiency. The results show that an anode pressure of 60–70 kPa and a 4% purging ratio can achieve not exceeding 5% ripple coefficient and not less than 35% system efficiency, and the optimum stack temperature is proved to be 54–60 °C. Adaptive purging strategies and temperature control strategies that vary with current are summarized which can be used for programming applications. Finally, these control strategies are executed under simulated loads to observe stack’s dynamic response and confirmed to maintain stable operation of the stack.

Evaluation of green banana pulp drying using infrared radiation with temperature control

The green banana flour (GBF) is a functional food rich in resistant starch and nutrients, probiotic, and has low glycemic index. The obtaining of the flour is usually done in dryers with high energy consumption, and possible deteriorating processes. These problems can be minimized with an adaptive temperature control system in infrared heat sources. The aim of this research was to quantify the energy savings in the production of green banana flour produced by infrared drying compared to the conventional process of drying in a resistance oven. The production of flour from green banana pulp was evaluated using an infrared radiation dryer with fruit surface temperature control (IR-FSTC). The IR-FSTC was performed by keeping the pulp surface at 50 °C and 60 °C. The results were compared with conventional drying using a resistive source. The energy consumption of drying using IR-FSTC was significantly reduced compared to the drying by resistive source and prevented the formation of hotspots. The drying process was also evaluated by physicochemical and spectroscopic analyses of the GBF. The product maintained the pH, titratable acidity, total soluble solids, and lipid content with values similar to those obtained by conventional heating oven drying. In turn, the spectroscopic analyses confirmed the maintenance of biomolecules inherent in GBF. Thus, it is concluded that the results contribute to the sustainable use of energy resources in the production of GBF.

All-weather anti-icing and de-icing properties of the carbon-based electro@photo-thermal material with adaptive temperature control performance

Photo-thermal anti/de-icing and electro-thermal anti/de-icing have been widely used in the field of anti/de-icing in recent years. The combination of electro@photo-thermal can achieve all-weather anti/de-icing. However, there is no need for anti/de-icing in hot environments, but the photo-thermal conversion does not automatically stop. And the photo-thermal conversion will cause the surface to overheat, seriously affecting the safety and normal operation of the machine, which is a great challenge in practical applications. Based on this, an electro@photo-thermal material with adaptive temperature control performance was proposed in this work. Specifically, the carbon cloth was used as an electro-thermal substrate, and the thermochromic powder mixed with polydimethylsiloxane (PDMS) was used as a photo-thermal component. The photo-thermal conversion experiment shows that when the temperature reaches the threshold temperature, the color of the material can be changed from black to white to reduce the absorption of sunlight, thus avoiding the overheating phenomenon. The anti/de-icing experiment also shows that the material meets the all-weather anti/de-icing requirements. In addition, continuous tape stripping experiments and acid-base soaking experiments also verified the excellent hydrophobicity of the material. The research provides new ideas for all-weather anti/de-icing systems with adaptive temperature control performance.

Adaptive PI control of temperature with natural ventilation in greenhouses using a bat algorithm variant

This paper presents a self-tuning PI controller to control the temperature of a greenhouse using natural ventilation. The PI controller parameters are adapted according to the changing dynamics of the process, identified with a simplified greenhouse temperature model based on first principles. The time-varying model parameters are estimated online using the random scaling-based bat algorithm. The model is linearized to obtain a first-order transfer function which facilitates the design of the PI controller using well-known tuning methods. Simulated results with real greenhouse data show that the proposed solution could be applied to keep controllers tuned throughout different agri-seasons.

Adaptive iterative learning control of internal temperature for high-speed trains

Adaptive iterative learning control of internal temperature for high-speed trains

An integrated framework for UAV-based precision plant protection in complex terrain: the ACHAGA solution for multi-tea fields.

UAV-based plant protection represents an efficient, energy-saving agricultural technology with significant potential to enhance tea production. However, the complex terrain of hilly and mountainous tea fields, coupled with the limited endurance of UAVs, presents substantial challenges for efficient route planning. This study introduces a novel methodological framework for UAV-based precision plant protection across multiple tea fields, addressing the difficulties in planning the shortest routes and optimal flights for UAVs constrained by their endurance. The framework employs a hyperbolic genetic annealing algorithm (ACHAGA) to optimize UAV plant protection routes with the objectives of minimizing flight distance, reducing the number of turns, and enhancing route stability. The method involves two primary steps: cluster partitioning and sortie allocation for multiple tea fields based on UAV range capabilities, followed by refining the UAV's flight path using a combination of hyperbolic genetic and simulated annealing algorithms with an adaptive temperature control mechanism. Simulation experiments and UAV route validation tests confirm the effectiveness of ACHAGA. The algorithm consistently identified optimal solutions within an average of 40 iterations, demonstrating robust global search capabilities and stability. It achieved an average reduction of 45.75 iterations and 1811.93 meters in the optimal route, with lower variation coefficients and extreme deviations across repeated simulations. ACHAGA significantly outperforms these algorithms, GA, GA-ACO, AFSA and BSO, which are also heuristic search strategies, in the multi-tea field route scheduling problem, reducing the optimal routes by 4904.82 m, 926.07 m, 3803.96 m and 800.11 m, respectively. Field tests revealed that ACHAGA reduced actual flight routes by 791.9 meters and 359.6 meters compared to manual and brainstorming-based planning methods, respectively. Additionally, the algorithm reduced flight scheduling distance and the number of turns by 11 compared to manual planning. This study provides a theoretical and technical foundation for managing large-scale tea plantations in challenging landscapes and serves as a reference for UAV precision operation planning in complex environments.

Development and application of the low Curie point positive temperature coefficient (PTC) electrothermal anti-icing material

The application of the positive temperature coefficient (PTC) materials in electrothermal anti-icing offers not only uniform heating of the anti-icing area, but also adaptive temperature control of the material. This study focuses on the preparation of a low Curie point PTC material with a Curie temperature point of 1 °C, using the in-situ polymerization method. The PTC behaviors of the material were extensively investigated by adjusting the component ratios and the microstructures of the materials were analyzed in correlation with scanning electron microscopy (SEM) images. Subsequently, adaptive temperature control and stability experiments were conducted to evaluate the material's performance. Eventually, the material was adopted to electrothermal anti-icing experiment for wind turbine blades. The results demonstrate that when the mass fraction of acetylene black is 9 wt%, the mass ratio of n-tetradecane/hydroxyl-terminated polydimethylsiloxane (HTPDMS) is 1:1, and the mass ratio of polydimethylsiloxane (PDMS)/HTPDMS is 1:2, the low-temperature resistivity of the low Curie point PTC material reaches an impressive value of only 5.37 Ω cm, the PTC intensity can reach 2.99, and the thermal conductivity can reach 0.778 W/(m·K). Furthermore, the material exhibits robust adaptive temperature control capability, effectively maintaining the temperature below 3 °C under different operating voltages and ambient temperatures. Even after 100 cyclic experiments, the material demonstrates superior PTC behavior, excellent temperature control ability, and exceptional stability. Additionally, under different operating voltages, ambient temperatures and wind speeds, the low Curie point PTC material exhibits excellent adaptive adjustment capability and maintains uniform heating of the anti-icing surface. These findings provide valuable insights for in-depth exploration of the low Curie point PTC electrothermal anti-icing materials.

Study on the low Curie point positive temperature coefficient (PTC) material based on the conductive model

PTC material is a positive temperature coefficient heat-sensitive material, and when used as a heating element for electrothermal anti-icing, it exhibits adaptive temperature control. This paper aims to establish and experimentally verify a conductive model for the low Curie point PTC material based on the distribution and blending characteristics of HTPDMS, PDMS, n-tetradecane and acetylene black. Additionally, various mass proportions of the low Curie point PTC materials in the conductive region are prepared and analyzed to understand their PTC effects. Finally, the feasibility of employing the low Curie point PTC material for electrothermal anti-icing is verified through anti-icing experiment. The results demonstrate that the resistivity of the materials with different mass proportions in the seepage and conductive regions conforms to the conductive model proposed in this study. Specifically, when the mass proportion of n-tetradecane/HTPDMS is 1:1, and the mass proportion of PDMS/HTPDMS is 1:2, the low Curie point PTC material exhibits a Curie temperature of 1 °C, with a low-temperature resistivity of only 5.37 Ω cm and a PTC intensity of 2.99. Notably, after subjecting the material to 100 thermal cycle experiments, there is no significant change in the Curie temperature point. The low-temperature resistivity slightly increases, while the PTC intensity declines marginally, and no obvious negative temperature coefficient (NTC) effect is observed, confirming the material's excellent stability. The findings of this study contribute to the understanding of the low Curie point PTC anti-icing materials and lay a solid foundation for their future application in anti-icing systems for fans, airplanes, and various surfaces.