Predictive Temperature Control Research Articles

Neural network predictive control of converter inlet temperature based on event‐triggered mechanism in flue gas acid production

AbstractThe process of smelting non‐ferrous metals results in significant emissions of flue gas that contains sulfur dioxide (SO), which is very harmful to the environment. Through precise control of converter inlet temperature, it is feasible to enhance the conversion ratio of SO and simultaneously mitigate environmental pollution by generating acid from flue gas. Because of the high degree of uncertainty in smelting process, converter inlet temperature is challenging to regulate and controller frequently needs updating. To improve control performance and decrease controller update times, an event‐triggered neural network model predictive control (ETNMPC) strategy is proposed. First, long short‐term memory (LSTM) prediction model and model predictive controller are developed. Second, it is decided whether to update the existing controller by designing an event‐triggered mechanism. Finally, using real data from a copper facility in Jiangxi Province, the temperature control experiment of converter inlet is carried out. Simulation results demonstrate that the proposed ETNMPC outperforms conventional time‐triggered method in terms of control performance, greatly lowers the times of controller updates, and significantly lowers computation costs and communication burden.

Low Temperature Combustion Modeling and Predictive Control of Marine Engines

The increase of popularity of reactivity-controlled compression ignition (RCCI) is attributed to its capability of achieving ultra-low nitrogen oxides (NOx) and soot emissions with high brake thermal efficiency (BTE). The complex and nonlinear nature of the RCCI combustion makes it challenging for model-based control design. In this work, a model-based control system is developed to control the combustion phasing and the indicated mean effective pressure (IMEP) of RCCI combustion through the adjustments of total fuel energy and blend ratio (BR) in fuel injection. A physics-based nonlinear control-oriented model (COM) is developed to predict the main combustion performance indicators of an RCCI marine engine. The model is validated against a detailed thermo-kinetic multizone model. A novel linear parameter-varying (LPV) model coupled with a model predictive controller (MPC) is utilized to control the aforementioned parameters of the developed COM. The developed system is able to control combustion phasing and IMEP with a tracking error that is within a 5% error margin for nominal and transient engine operating conditions. The developed control system promotes the adoption of RCCI combustion in commercial marine engines.

Multi‐model predictive control of converter inlet temperature in the process of acid production with flue gas

SummaryThe smelting of non‐ferrous metals produces substantial quantities of sulfur dioxide (SO)‐laden flue gas, which is seriously harmful to environment and humans. To improve the conversion ratio of SO and minimize environmental pollution, controlling converter inlet temperature during acid production has proven to be an efficient approach. However, unsteadiness of smelting procedure leads to frequent changes in the concentration of SO, which affects the catalytic conversion of SO and the production of sulfuric acid. To regulate converter inlet temperature, a proposed method of multi‐model predictive control is introduced. First, working conditions are divided and characterized according to the range of SO concentration. Then, the mathematical model is established for each working condition and the model predictive controller is designed. Finally, an effective switching mechanism is established to realize smooth switching under different working conditions and closed‐loop control of the whole system. Through simulation validation, compared with traditional single‐model predictive controllers and multi‐model PID controllers, the proposed approach demonstrates improved transient performance and steady‐state performance. Simulation outcomes clearly highlight the superiority of the proposed algorithm.

Model Predictive Control of Humidity Deficit and Temperature in Winter Greenhouses: Subspace Weather-Based Modelling and Sampling Period Effects

Generally, windows in greenhouses are automatically opened and closed to regulate the internal temperature. However, because the air outside during the winter in Japan is dry, opening windows to reduce the temperature causes the humidity deficit to increase above 6 g/m3, thereby inhibiting plant growth. Therefore, in this study, we developed a model that considers the effects of weather and the sampling period using a subspace (N4SID) method based on environmental data from inside and outside a greenhouse during winter. By adopting a data-driven model, models for greenhouse temperature and humidity deficits can be constructed conveniently. First, four models incorporating weather conditions were constructed over a 28-day modelling period. Moreover, the average root mean square error values from 8:00 to 16:00 during the 10-day model evaluation period were examined. Subsequently, model predictive controllers were developed from the four models with sampling periods of 1, 2, 4, and 8 min, and their performances were compared over the model evaluation period. The model predictive controller with a sampling period of 4 min was the most energy-efficient, achieving control of the humidity deficit of up to at most 6 g/m3 (close to the target value of 4.5 g/m3) while maintaining the target temperature of 26 °C.

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

Practical Application of Model Predictive Zonal Temperature Estimation and Control for Vehicle Cabins

Predictive temperature control design for reaction calorimeter based on mechanism parameter model.

Isothermal control is the most basic and crucial function in the principle of a reaction calorimeter system and affects the speed and validity of the calorimetric experiment. However, the complex and uncertain working conditions in different reaction processes pose a challenge to the adaptability of temperature control algorithms. Aiming at the problem, a heat transfer model of the system is first established for temperature control design. From the simulation results, a prediction model based on equivalent mechanism parameters is determined for the control. Then, an integrated model predictive control (MPC) strategy is presented. To reduce the influence on the temperature control caused by the mismatch of the prediction model, a set of online parameter identification and adjustment methods is proposed. Simulations of the MPC control were implemented to analyze the control's performance. Experiments were also carried out to verify the advantages of the proposed strategy over the proportional-integral-derivative algorithm and demonstrate the role and efficiency of online identification. This control strategy can be applied to other laboratory-scale instruments with tank reactors.

Model predictive temperature control of a closed-loop spray cooling system

A dynamic model of a closed-loop spray cooling system was proposed, and a model predictive temperature control algorithm was presented. The presented algorithm could quantitively estimate sudden changes in heat load, and then plan an optimized control path along which the temperature trajectory was numerically predicted to be the most stable. Then, the presented algorithm was integrated into a closed-loop spray cooling system, and its control performance was numerically studied. The results showed that, following a 20 W downward step change in heat load, the temperature was re-stabilized in 4 s, with its oscillation less than 0.14 K. Under identical conditions, the presented algorithm outperformed PID in terms of temperature stability, with its peak time shorter by 25–55%, settling time shorter by 38–51%, and average temperature offset smaller by 50–60%. This advantage was mainly because the presented algorithm compensated for hydraulic and thermal lags, two sources of control nonlinearity, by excessive regulation in advance. Unexpectedly, temperature overshoots of the presented algorithm and PID did not differ significantly, seemingly due to the algorithm-inherent estimation lag. From a system design perspective, the length of pump pipeline should be as short as possible, which proved to help reduce overshoot and settling time. Similarly, addition in heat load’s mass also helped reduce overshoot, but would result in an unfavorable long settling time, and vice versa. The simulation results were validated by experimental data, with error less than 10%.

Constrained temperature and relative humidity predictive control: Agricultural greenhouse case of study

The importance of Model Predictive Control (MPC) has significant applications in the agricultural industry, more specifically for greenhouse’s control tasks. However, the complexity of the greenhouse and its limited prior knowledge prevent an exact mathematical description of the system. Subspace methods provide a promising solution to this issue through their capacity to identify the system’s comportment using the fit between model output and observed data. In this paper, we introduce an application of Constrained Model Predictive Control (CMPC) for a greenhouse temperature and relative humidity. For this purpose, two Multi Input Single Output (MISO) systems, using Numerical Subspace State Space System Identification (N4SID) algorithm, are firstly suggested to identify the temperature and the relative humidity comportment to heating and ventilation actions. In this sense, linear state space models were adopted in order to evaluate the robustness of the control strategy. Once the system is identified, the MPC technique is applied for the temperature and the humidity regulation. Simulation results show that the regulation of the temperature and the relative humidity under constraints was guaranteed, both parameters respect the ranges 15 °C ≤ Tint ≤ 30 °C and 50 % ≤ Hint ≤ 70 % respectively. On the other hand, the control signals uf and uh applied to the fan and the heater, respect the hard constraints notion, the control signals for the fan and the heater did not exceed 0 ≤ uf ≤ 4.3 Volts and 0 ≤ uh ≤ 5 Volts, respectively, which proves the effectiveness of the MPC and the tracking tasks. Moreover, we show that with the proposed technique, using a new optimization toolbox, the computational complexity has been significantly reduced. The greenhouse in question is devoted to Schefflera Arboricola cultivation.

A Novel Concept of Transperineal Focused Ultrasound Transducer for Prostate Cancer Local Deep Hyperthermia Treatments

Design, embodiment, and experimental study of a novel concept of extracorporeal phased array ultrasound transducer for prostate cancer regional deep hyperthermia treatments using a transperineal acoustic window is presented. An optimized design of hyperthermia applicator was derived from a modelling software where acoustic and thermal fields were computed based on anatomical data. Performance tests have been experimentally conducted on gel phantoms and tissues, under 3T MRI guidance using PRFS thermometry. Feedback controlled hyperthermia (ΔT = 5 °C during 20min) was performed on two ex vivo lamb carcasses with prostate mimicking pelvic tissue, to demonstrate capability of spatio-temporal temperature control and to assess potential risks and side effects. Our optimization approach yielded a therapeutic ultrasound transducer consisting of 192 elements of variable shape and surface, pseudo randomly distributed on 6 columns, using a frequency of 700 kHz. Radius of curvature was 140 mm and active water circulation was included for cooling. The measured focusing capabilities covered a volume of 24 × 50 × 60 mm3. Acoustic coupling of excellent quality was achieved. No interference was detected between sonication and MR acquisitions. On ex vivo experiments the target temperature elevation of 5 °C was reached after 5 min and maintained during another 15 min with the predictive temperature controller showing 0.2 °C accuracy. No significant temperature rise was observed on skin and bonny structures. Reported results represent a promising step toward the implementation of transperineal ultrasound hyperthermia in a pilot study of reirradiation in prostate cancer patients.

Extended state observer-based model predictive temperature control of mechanically pumped two-phase loop: An experimental study

Mechanically pumped two-phase loop (MPTL), as an emerging two-phase heat dissipation transformational technique, has made booming progress in some critical but tough cases such as space stations and high-performance chips. However, accurate temperature regulation of MPTL is intractable due to periodic heat load disturbance, measurement noise, and micro-channel boiling nonlinearity. To address these issues, an extended state observer-based model predictive control (ESOMPC) strategy is developed in this paper. To describe the process model, this paper builds an MPTL platform equipped with a micro-channel evaporator, based on which a series of sinusoidal heat load variation is carried out to show the effects on the wall temperature. Open-loop step experiments are then conducted in terms of flow rate of the working fluid so that the process model can be identified and the nonlinearity can be analyzed. Then, the ESOMPC is developed by incorporating the disturbance information in the optimization framework of the MPC. The ESO part is utilized to estimate the unknown disturbances and compensate the nonlinearity of MPTL, and thus enhance the disturbance mitigation property of the MPC algorithm. Finally, the closed-loop experiments verifies the efficacy of the proposed solution, showing smaller temperature fluctuation and shorter settling time than the conventional controller. The results indicate the great potential of the proposed ESOMPC in enhancing the temperature control accuracy of MPTL in terms of thermal management performance.

Multi-model adaptive predictive control of superheated steam temperature of DSG solar parabolic-trough collector based on heat-steam ratio and its reference trajectory

The direct steam generation (DSG) solar thermal power generation system has obvious inertia and nonlinearity in the response of superheated steam temperature. Due to the intermittent and random fluctuations of solar irradiation, the DSG system frequently undergoes large changes in operating conditions, which results in a very complicated problem of controlling the superheated steam temperature at the collector outlet. This paper proposed a multi-model adaptive control scheme based on the heat-steam ratio and its reference trajectory for the control problem of the outlet steam temperature of the DSG solar parabolic-trough collector. Firstly, through analyzing the dynamic characteristics of the superheated steam temperature, the main factors and the main nonlinear characteristics that lead to the nonlinearity of the steam temperature response are revealed. Secondly, taking the ratio of solar irradiation to the outlet steam flow (heat-steam ratio) as the characteristic parameter based on the nonlinear characteristics of the system, the DSG collector system is divided into several linearized subspaces, and then the corresponding steam temperature predictive sub-model set and steam temperature adaptive predictive control scheme are established. Finally, a smooth switching strategy based on the reference trajectory of the nonlinear characteristic parameter (heat-steam ratio) is established in response to the frequent or large switching of the controller caused by the large changes in the operating conditions, which effectively improved the stability of the temperature control process of the superheated steam at the DSG collector outlet. In this paper, the effectiveness of the above-mentioned control scheme has been preliminarily verified through simulation experiments.

Stochastic configuration network based cascade generalized predictive control of main steam temperature in power plants

The main steam temperature (MST) in power plants suffers from nonlinearity and large time delay, which cause large overshoot and long settling time under widely used cascade proportion integration differentiation (PID) controller. In order to cope with the negative effects, we propose a stochastic configuration network (SCN) based cascade generalized predictive control (GPC) scheme to improve the performance of the MST. A three-layer SCN is employed to model the MST process. The SCN is constructed by two phases, i.e., initial phase and real-time phase. The initial phase determines the structure and primary parameters of the learner model using the stochastic configuration algorithm. The real-time phase employs weighted recursive least squares (WRLS) for building the real-time MST process model for GPC design. Taking into account some constraints of the MST process, Karush–Kuhn–Tucker (KKT) conditions are applied for solving the constrained receding-horizon optimization problem. The derived explicit solutions of GPC avoid the implicit form which usually has to be solved iteratively. Comparative simulations demonstrate the superiority of the proposed SCN based cascade GPC (SCN-CGPC). Finally, the proposed SCN-CGPC is implemented via a standalone external MST control system in a power plant. The effectiveness and practicability are validated with the real-world application.

Feed-forward offset-free model predictive temperature control for proton exchange membrane fuel cell: An experimental study

Safe and economic operation of an open-cathode proton exchange membrane fuel cell (PEMFC) requires an efficient thermal management strategy. The stack temperature regulation in PEMFC is, however, challenging due to often stringent set-point tracking tasks, frequent load fluctuations, constrained manipulated variables, and various modeling uncertainties and nonlinearities. To this end, a feed-forward offset-free model predictive control (MPC) approach, aiming at uncertainties resolving and disturbance mitigation, is developed to simultaneously address the above difficulties. In the proposed framework, the information about the measured power load fluctuations is used in the optimization algorithm as feed-forward information to eventually mitigate the influence of load fluctuations on the controlled output and increases the overall control quality. Additionally, the unmodeled dynamics and the other unmeasurable disturbances/uncertainties are collectively considered as an extended state of the system (to achieve zero static errors) and the on-line reconstructed aggregated disturbances is continuously sent to the MPC algorithm to increase its optimization performance and to achieve offset-free control objectives. The obtained results are quantitatively compared with conventional control strategies for PEMFCs, including a model-based PI controller, its modification utilizing disturbance feed-forward, and a standard offset-free MPC (i.e. without feed-forward). Both the simulations, realized in MATLAB/Simulink, and hardware experiments, conducted on a 500 W PEMFC testbed, show excellence of the proposed feed-forward offset-free MPC consisting in faster temperature tracking and higher robustness. The obtained satisfactory results show the introduced control solution to be a promising prospect and help accelerating further applications of PEMFCs.

Constrained discrete model predictive control of a greenhouse system temperature

In this paper, a constrained discete model predictive control (CDMPC) strategy for a greenhouse inside temperature is presented. To describe the dynamics of our system’s inside temperature, an experimental greenhouse prototype is engaged. For the mathematical modeling, a state space form which fits properly the acquired data of the greenhouse temperature dynamics is identified using the subspace system identification (N4sid) algorithm. The obtained model is used in order to develop the CDMPC starategy which role is to select the best control moves based on an optimization procedure under the constraints on the control notion. For efficient evaluation of the proposed control approach Matlab/Simulink and Yalmip optimization toolbox are used for algorithm and blocks implementation. The simulation results confirm the accuracy of the controller that garantees both the control and the reference tracking objectives.

Demand Side Electric Energy Consumption Optimization in a Smart Household Using Scheduling and Model Predictive Temperature Control

Abstract Utility companies seek to increase energy efficiency and productivity and try to reduce peak loads. This often involves consumer-side demand management in residential areas using dynamic time-of-use (ToU) tariff. Such strategies work if the consumer-side response is at least partly automated using some real-time optimization strategy. Our paper proposes a consumer-side optimization and control framework for scheduling the electric appliances in a smart household and preserving a thermal comfort level through an electric heating system. Our framework consists of two optimization components interacting with each other. The first optimization component schedules the home appliances based on a mixed integer programming approach. An electric vehicle (EV) is considered as a special home appliance with an energy storage capability. The second optimization component is the model predictive control (MPC) strategy for the electric heating system, such that the input constraints are defined by the scheduling results of the first component. Due to outside temperature variations, the input constraints may impede the MPC to maintain the required thermal comfort, which triggers a rescheduling event for the first component. The efficiency of the framework is presented in multiple simulations for scenarios with different consumer behaviors.

Adaptive predictive control of average coolant temperature based on chaotic particle swarm optimization

Aiming at the problem that the average temperature control system of nuclear reactor coolant under variable conditions is nonlinear and time-varying, and the traditional fixed model cannot meet the control requirements, an improved generalized predictive control method with adaptive identification of model parameters is proposed. First, the structure of the coolant average temperature model is determined through mechanism derivation, and the forgetting factor recursive least square algorithm is used to identify the model parameters online and in real time. Secondly, the chaotic particle swarm algorithm is used to optimize the control increment during rolling optimization. The simulation is carried out on the MATLAB platform, and the results show that the tracking ability of the average coolant temperature is significantly better than the traditional PI control under variable conditions.

Non-Linear Model Predictive Control of Cabin Temperature and Air Quality in Fully Electric Vehicles

This article describes an application of Non-linear Model Predictive Control algorithms on energy efficient control of fully electric vehicle cabin temperature and air quality. Since fully electric vehicles can not utilize waste heat from a powertrain (or there is not enough waste heat) as ICE vehicles do, it is necessary to employ advanced control approaches (especially for cabin heating) due to the possible mileage lost by using energy from the batteries for cabin conditioning. The basic idea behind this is to avoid the heat losses caused by excessive air exchange and to ensure a satisfactory air quality in combination with a user defined temperature. The Non-linear Model Predictive control algorithms were successfully implemented into an Infineon AURIX Tricore microcontroller and tested within a Processor in the Loop simulation.

Model Predictive Temperature and Humidity Control of Greenhouse with Ventilation

Facility gardening with complex environmental control systems has potential in improving productivity and producing high value-added agricultural products; however, its introduction is expensive. Thus, this study proposes a greenhouse temperature and humidity control system using relatively inexpensive heaters, humidifiers, and ventilation fans. Humidity control is expected to promote photosynthesis and prevent plant disease. The concept of model predictive control (MPC) was applied to a small greenhouse to minimise the sum of squared errors. Compared with the two conventional methods, the simulation results showed that MPC reduced the relative RMS error of the temperature and humidity deficit to 23.5% and 13.1%, respectively.

Автоматизированная система регулирования температуры с учетом прогнозируемых изменений метеоусловий

An analysis of the available automated indoor temperature control systems shows that all of them have a significant shortage. The systems do not provide for predictive temperature control in an automated form. Such changes are determined and made, depending on the qualification, by the operator based on personal experience and the reports received by him on the weather channels. Possible errors lead to loss of productivity or disruption of technological modes of production. (Research purpose) The research purpose is in developing a system of automated predictive control of the temperature regime of industrial premises to improve energy efficiency and the quality of maintaining the necessary climatic conditions. (Materials and methods) Increasing the efficiency of greenhouses can be achieved through the introduction of an automated and more economical temperature control system. The proposed device additionally includes a weather forecast receiving unit, made on the basis of microprocessor technology, for reliable signal reception. The air that circulates inside the greenhouse through heat exchange circuits was used as a heat carrier. (Results and discussion) An additional heat (cold) reserve is created in the greenhouse, which allows to maintain the set temperature in the room, taking into account the weather forecast. The results presented in patents No. 2710010, 2667684 and the implemented control system based on certificates of state registration of programs for electronic computers No. 2015618030, 20146115490, and 2014617605 exceed the results obtained by the authors of the proposed developments based on patents No. 2586923 and No. 80308. (Conclusions) The use of new technical solutions and technologies for providing the basis for the development of a new principle for the construction of automated control systems for the temperature regime of industrial premises, taking into account the predicted changes in external conditions. The practical result of the conducted research is: a concept, a control algorithm, a manufactured and tested experimental sample of a device for controlling the temperature regime in a greenhouse.

Stepped generalized predictive control of test tank temperature based on backpropagation neural network

It is significant to ensure the temperature stability of the test tank, which has a direct impact on reducing production energy consumption, improving tank quality and ensuring experimental results. However, the high nonlinearity and long delay of the temperature control process in the test tank make it difficult to satisfactorily control the temperature by traditional control methods. To solve the problem, this paper designs a temperature prediction model for the test tank based on backpropagation neural network (BPNN), which has a good fitting ability for nonlinear systems. The proposed model was coupled with improved generalized predictive control (GPC) into a new test tank temperature control method, namely, BPNN-based stepped GPC. Simulation results show that the new control method could reduce the prediction error of the BPNN and effectively control the temperature of the test tank.