Energy utilization efficiency evaluation model of refining unit Based on Contourlet neural network optimized by improved grey optimization algorithm

Energy ◽  
2019 ◽  
Vol 185 ◽  
pp. 1032-1044 ◽  
Author(s):  
Bin Zhao ◽  
Yi Ren ◽  
Diankui Gao ◽  
Lizhi Xu ◽  
Yuanyuan Zhang
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Evaluation Model ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Efficiency Evaluation ◽  
Refining Unit

scholarly journals An Energy Efficiency Index Formation and Analysis of Integrated Energy System Based on Exergy Efficiency

2021 ◽  
Vol 9 ◽  
Author(s):  
Huiling Su ◽  
Qifeng Huang ◽  
Zhongdong Wang
Keyword(s):  
Energy Efficiency ◽  
Evaluation Model ◽  
Second Law Of Thermodynamics ◽  
Energy System ◽  
Efficiency Index ◽  
Exergy Efficiency ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Efficiency Evaluation ◽  
Integrated Energy System

In the context of the energy crisis and environmental deterioration, the integrated energy system (IES) based on multi-energy complementarity and cascaded utilization of energy is considered as an effective way to solve these problems. Due to the different energy forms and the various characteristics in the IES, the coupling relationships among various energy forms are complicated which enlarges the difficulty of energy efficiency evaluation of the IES. In order to flexibly analyze the energy efficiency of the IES, an operation efficiency evaluation model for the IES is established. First, energy utilization efficiency (EUE) and exergy efficiency (EXE) are proposed based on the first/second law of thermodynamics. Second, the energy efficiency models for five processes and four subsystems of the IES are formed. Lastly, an actual commercial-industrial park with integrated energy is employed to validate the proposed method.

Evaluation of Energy Efficiency for a CCHP System With Available Microturbine

2007 ◽  
Author(s):  
H. X. Liang ◽  
Q. W. Wang
Keyword(s):  
Gas Turbine ◽  
Waste Heat ◽  
Energy System ◽  
Primary Energy ◽  
Economic Benefits ◽  
Optimal Operation ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Energetic Efficiency ◽  
Efficiency Evaluation

This paper deals with the problem of energy utilization efficiency evaluation of a microturbine system for Combined Cooling, Heating and Power production (CCHP). The CCHP system integrates power generation, cooling and heating, which is a type of total energy system on the basis of energy cascade utilization principle, and has a large potential of energy saving and economical efficiency. A typical CCHP system has several options to fulfill energy requirements of its application, the electrical energy can be produced by a gas turbine, the heat can be generated by the waste heat of a gas turbine, and the cooling load can be satisfied by an absorption chiller driven by the waste heat of a gas turbine. The energy problem of the CCHP system is so large and complex that the existing engineering cannot provide satisfactory solutions. The decisive values for energetic efficiency evaluation of such systems are the primary energy generation cost. In this paper, in order to reveal internal essence of CCHP, we have analyzed typical CCHP systems and compared them with individual systems. The optimal operation of this system is dependent upon load conditions to be satisfied. The results indicate that CCHP brings 38.7 percent decrease in energy consumption comparing with the individual systems. A CCHP system saves fuel resources and has the assurance of economic benefits. Moreover, two basic CCHP models are presented for determining the optimum energy combination for the CCHP system with 100kW microturbine, and the more practical performances of various units are introduced, then Primary Energy Ratio (PER) and exergy efficiency (α) of various types and sizes systems are analyzed. Through exergy comparison performed for two kinds of CCHP systems, we have identified the essential principle for high performance of the CCHP system, and consequently pointed out the promising features for further development.

Classroom education effect evaluation model based on MFO intelligent optimization algorithm

2020 ◽  
pp. 1-12
Author(s):  
Ouyang Weimin
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Evaluation Model ◽  
Search Space ◽  
Effect Evaluation ◽  
Intelligent Optimization ◽  
Education Effect ◽  
Intelligent Optimization Algorithm ◽  
Classroom Education

In order to improve the evaluation effect of classroom education, this paper proposes the MFO intelligent optimization algorithm based on the idea of machine learning, and builds the classroom education effect evaluation model based on the MFO intelligent optimization algorithm. Moreover, this paper uses a logarithmic spiral to simulate the path of the moth to the flame and invert the pending parameters in the mathematical model, and adds vertical and horizontal algorithms and chaos operators on this basis. The crisscross algorithm allows different moth individuals and the same moth to perform cross calculations with different computing dimensions to increase the diversity of moth populations, so that moths in the search space can traverse the entire search space as much as possible to find a better solution. Moreover, in view of the problems of BP neural network such as low fitting accuracy, this paper applies the CCMFO algorithm to improve the BP neural network to form the CCMFO-BP algorithm, and improves the weight and threshold update process of the BP neural network to make the network operation more efficient and accurate. Finally, this paper designs experiments to analyze the performance of the model constructed in this paper. The research results show that the model constructed in this paper meets the expected requirements.

scholarly journals AHP Based Evaluation Model of Energy Efficiency for Energy Internet

2021 ◽  
Vol 233 ◽  
pp. 01031
Author(s):  
Qing Wang ◽  
Song Liu ◽  
Congcong Li ◽  
Chao Yu ◽  
Yanxi Liu
Keyword(s):  
Energy Efficiency ◽  
Evaluation Method ◽  
Solar Power ◽  
Evaluation Model ◽  
Evaluation Index System ◽  
Energy Utilization ◽  
Utilization Efficiency ◽  
Analytic Hierarchy ◽  
Energy Internet ◽  
Improve Energy Efficiency

The emerging construction of China’s industry park requires for a specific evaluation method for the local energy internet’s power utilization efficiency. This article focuses on the solar power exploitation from power supply reliability, energy utilization efficiency, solar power general efficiency, established a complete energy internet evaluation index system using improved analytic hierarchy process (AHP). Such method was proved to be more delicate to evaluate the solar-involved park energy internet comparing to traditional methods. The energy internet of a certain industry park was taken as an example for verification. It was found that the improved AHP method can objectively reflect the planning’s efficiency from multi-indicators. When evaluating from different perspective, focusing on a different characteristic of the regional power grid will lead to changes in the AHP evaluation weighting and the results. According to the evaluation result, the relevant suggestions to improve energy efficiency were also proposed.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

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