Energy-saving Optimization Control Research Articles

Water Heating and Circulating Heating System with Energy-Saving Optimization Control

Over the past few years, due to an aging population and a longer life expectancy, traditional high-oil, high-calorie, and high-temperature culinary concepts have been widely replaced by healthier, low-temperature heating methods. However, while the key device of the Sous-Vide heating system ordinarily operates at full power to achieve the target temperature, pump speed control is not currently considered within the water heating and circulating system framework. This study develops a model for a water heating and circulating system and examines the characteristics of the lowest power point and pump speed. Building upon these results, we present the LPPT control method as a means of optimizing input power for heating. The effectiveness of this method is supported by simulations and experiments, which demonstrate a significant reduction in energy consumption. The control concept calculates the real-time input power based on the input voltage and current, and it can achieve the most efficient input power by perturbing the pump speed. It is demonstrated that applying LPPT to daily pot capacity reduces the Sous-Vide Cooker’s input power by up to 17% and achieves efficiency optimization control by removing the need to calculate the foods and other parameters of the water heating and circulating system environment.

Research progress and prospect of energy-saving optimal control for intelligent and connected electric vehicles

Research progress and prospect of energy-saving optimal control for intelligent and connected electric vehicles

Hierarchical energy-saving optimization control of networked hybrid electric vehicles under urban Conditions

Hierarchical energy-saving optimization control of networked hybrid electric vehicles under urban Conditions

Research on Energy-saving Optimization Control Method in Wastewater Treatment Process

Research on Energy-saving Optimization Control Method in Wastewater Treatment Process

Energy-saving optimization control method of commercial building double cold source air-conditioning units

In order to improve the energy-saving level of commercial buildings? air-conditioning units with double cold sources and reduce the energy cost, an energy-saving optimal control method of commercial buildings? air-conditioning units with double cold sources based on self-organizing neural network is proposed. Constrained parameter model of energy-saving control of commercial buildings? double-cold-source air-conditioning units is constructed. Taking the balanced real-time dynamic adjustment characteristic quantity of inverter of commercial buildings? double-cold-source air-conditioning units as the control object, combined with the fuzzy constraint method of variable frequency speed adjustment parameters and stroke adjustment power of commercial buildings? double-cold-source air-conditioning units, the steady-state adjustment of energy-saving output of commercial buildings? double-cold-source air-conditioning units is realized, and the self-organizing neural network control model of periodic potential energy load of building HVAC system is established. Combined with the small disturbance suppression method of motor rated power, the optimal control design of energy-saving of commercial buildings. The simulation results show that the output stability of the energy-saving control of commercial building air-conditioning units with double cold sources is good, the steady-state working ability is strong, and the energy-saving effect of commercial building air-conditioning units with double cold sources is good.

Application of energy-saving control strategy in air conditioning terminal equipment based on constant temperature difference of chilled water

Effectively reducing the energy consumption of central air conditioning systems is an important aspect of building energy efficiency. The energy consumption of terminal equipment accounts for over 25% of that of air conditioning systems. Although the heat and moisture exchange mechanism of the terminal unit has been extensively studied, studies on control strategies for terminal unit operation are lacking. This study proposes an energy-saving optimization control strategy for terminal units based on the constant temperature difference of chilled water in an engineering application study. A fan coil unit (FCU) controller was designed and manufactured, and a Local Area Network (LAN) control system was constructed and compared to determine the optimal control strategy. When adopting the energy-saving optimization control strategy for the terminal unit based on a constant chilled water temperature difference, the energy consumption of the terminal equipment was significantly decreased by 7.2%. The flow rate of chilled water was reduced by 26.94% when the indoor air temperature requirements were met, effectively reducing the energy consumption of the central air-conditioning system. The adjustment time required to reach a stable indoor temperature was reduced and environmental comfort was improved. These results demonstrate the potential of this control strategy for use in engineering practices to replace traditional control modes.

System of Energy-Saving Optimal Control of Metal Heating Process in Heat Treatment Furnaces of Rolling Mills

Heat-treatment furnaces for workpiece pre-rolling heating are heating plants of the transition type where sheet slabs with various characteristics are simultaneously heated. The heat treatment furnace performance is directly connected with the efficient operation of the rolling mill process plants. The irregular operation pace of the rolling mill complicates the implementation of energy-saving workpiece pre-rolling heating modes and increases the risk of delivering an insufficiently heated workpiece. This paper proposes the system of controlling the heat mode of the heat-treatment furnace by the criterion of fuel consumption rate minimization and controlling over a real heated state of a sheet slab located at the workpiece pushing from the heat-treatment furnace to the rolling mill. The author uses a simplified mathematical model of workpiece heating intended for defining the energy-saving heating path accounting for the impact of technological and structural constraints in the workpiece heating process. The calculation is conducted in real time and allows obtaining an optimal heating path of each workpiece at the moment of its loading into the heat-treatment furnace. The paper considers the necessity of interaction and the interaction itself of the subsystems performing the key function in the energy-saving operation mode of the heat-treatment furnace at workpiece heating. The author studied the subsystems of forecasting the workpiece heating time on the state of the rolling mill equipment and the geometrical dimensions of the rolled band, control over gaseous fuel burning in the heat-treatment furnace operation space, system of control over the workpiece heating state before pushing from the heat-treatment furnace and forecasting the roll temperature after the treatment of the rolling mill roughing train.

基于多目标规划的中央空调系统优化控制研究

为了提高中央空调系统的工作效率，同时降低能源消耗，需要对中央空调系统进行节能优化控制研究。现有的中央空调系统控制策略很难实现实时控制，且优化控制效果较差。为此，提出了基于多目标规划的中央空调系统动态优化方法。该方法先研究了中央空调系统各变量的变化规律，并对数据进行降维；然后，使用多元回归分析方法得到变量的回归方程；在此基础上，以设备运行状态和转速为决策变量，以系统总耗电量和系统效率为目标函数，在设备转速、状态、系统稳定性和设备运转等约束条件下，建立了基于多目标规划的中央空调系统动态优化模型。优化结果显示，所提方法优化控制效果较好，有效地降低了系统总耗电量。 In order to improve the efficiency of central air conditioning system and reduce energy consump-tion, it is necessary to study the energy-saving optimization control of the central air conditioning system. However, the current control strategy of central air conditioning system is difficult to realize real-time control, and has the problem of poor optimization control. Therefore, we propose an optimization control method for central air conditioning system based on multi-objective programming. Firstly, we study the changing rules of the variables in the central air conditioning system, and reduce the dimension of the data. Then, we used the multiple regression analysis to obtain the regression equation of the variable. On the basis of the above, we use the operating condition and speed of the equipment as the decision variables, and take the total energy con-sumption and the system efficiency as the objective function. Under the constraints of equipment speed, state, system stability and equipment operation, we establish the optimization control model of the central air conditioning system based on the multi-objective programming model. Finally, the optimization result shows that the optimized control effect of our proposed method is better, and it can effectively reduce the total power consumption.

Energy-saving optimal control over heating of continuous cast billets

This paper describes a solution to the problem of energy-saving optimal control over heating of continuous cast billets in a transient operation of high-performance hot rolling mills. We set forth a technique for forecasting a total time of heating by the moment of charging billets into a furnace and suggested a technique for prompt calculation and optimal control following the surface temperature of heated billets subject to generation of set points for temperature controllers in heating areas and objective control of a thermal state of every heated billet before its discharge from the furnace and forecast of expected temperature of the feedstock after roughing mills. All studied techniques focus on software implementation entailing insignificant economic costs and guaranteed success.

The study of the dynamic load forecasting model about air-conditioning system based on the terminal user load

The energy-saving optimization control strategies of central air-conditioning system widely used now is according to the operation parameters of the cold source side to adjust the load supply. But the operation parameter describes the centralized and hysteresis effect about the central air conditioning system in current load. The effects can’t well reflect the actual change about the user load and ensure the comfort of all users. It will be better if directly regulate the cold source of central air conditioning load according to the needs of the terminal users. Based on this idea, the study calculates the load of the terminal air-conditioning equipment according to its operating parameters, and then the dynamic load forecasting methods is used to establish the forecasting model that can forecast in real-time the load demand in the next time. The load forecasting methods do not need to analyze the factors that directly affect indoor load changes and to establish the load changes physical model. It use “the degree of load changes” to describe the load variation influence so to avoid the randomness and uncertainty about the change of the air-conditioning system load. The experimental results show that the forecasting model has well prediction effect. With the support of network control technology, all the terminal user load demand can be got, so the total load about the central air-conditioning based the terminal user forecasting load can be got. The better energy-saving optimization control strategies of central air-conditioning system will be get to use the forecasting load as the basis of the energy saving control for the central air conditioning cold source system.

Strategy of Intelligent Energy-Saving Optimization Control and Energy Consumption Analysis on Regulator Control System of Closed Greenhouse

Adopting the integrated TOPSIS intelligent energy optimization control strategy, and compared to conventional single control strategy on energy consumption of greenhouse equipment under closed condition, this paper arrives at the best energy saving optimization control strategy with comprehensive benefits. The result shows that, integrated intelligent optimizing control was obviously more energy saving compared to those did not take optimization control. Specific results as follows: TOPSIS integration strategy with energy saving of 725.39kwh, energy-saving rate of 44.19%.This shows that the proposed integrated intelligent energy optimization control strategy and energy saving effect is remarkable.

Energy-saving optimal control for a factual electrostatic precipitator with multiple electric-field stages based on GA

For energy-saving control of electrostatic precipitator with multiple electric-field stages, a new optimal control strategy to minimize the total power consumption by adjusting the secondary voltages subject to the dust outlet concentration requirement is suggested. The optimal problem is established on the basis of the proposed mathematical models on the secondary voltage relating to the outlet concentration, the secondary current, the primary voltage and the primary current, and is solved by the genetic algorithm. A practice application to an industrial precipitator is given as well.

Research on Control Strategy for Energy-Saving Optimization Algorithm of the Hydraulic Hybrid Vehicle

A new structure of hydraulic hybrid vehicle (HHV) with hydraulic transformer (HT) was built and the working principle of the new hydraulic hybrid vehicle was described. According to the operating characteristics of HT and energy-saving optimization conditions of accumulator used the HHV; Energy-saving optimization control algorithm with various operation conditions in different working conditions of vehicle was established. Then, simulation analysis to control performance of energy-saving algorithm was carried out using PID, Fuzzy Logic Controller (FLC) and Fuzzy-PID control strategy. Results show that Fuzzy-PID controller has a small influence on the parameters of energy-saving optimization algorithm of the hydraulic hybrid vehicle and maximizing energy recovery can be achieved in different energy states by Fuzzy-PID controller.

Building Cooling Load Forecasting Based on Support Vector Machines with Simulated Annealing

Accurate forecasting of building cooling load has been one of the most important issues in the electricity industry. Recently, along with energy-saving optimal control, accurate forecast of electricity load has received increasing attention. Because of the general nonlinear mapping capabilities of forecasting, artificial neural networks have played a crucial role in forecasting electricity load. Support vector machines (SVMs) have been successfully employed to solve nonlinear regression and time series problems. In order to improve time efficiency of prediction, a new hourly cooling load prediction model and method based on Support Vector Machine in this paper. Moreover, simulated annealing (SA) algorithms were employed to choose the parameters of a SVM model. Subsequently, examples of cooling load data from Guangzhou were used to illustrate the proposed SVM-SA model. A comparison of the performance between SVM optimized by Particle Swarm Optimization (SVM-PSO) and SVM-SA is carried out. Experiments results demonstrate that SVM-SA can achieve better accuracy and generalization than the SVM-PSO. Consequently, the SVM-SA model provides a promising alternative for forecasting building load.