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Power demands problem, zero reserved margin and depletion in coal production has seen the adoption of renewable energy technologies (RET) increase. Unlike conventional energy sources, RET are unpredictable and affected by non-linear, complex factors making it challenging to predict their daily/monthly supply contribution. Most available techniques seems to lack the aptitude to handle contribution...
Focus on the first China domestic coking flue gas desulfurization and denitriation integrated device, in order to solve the problem that the entrance parameters fluctuate and a detection lag exists due to the upstream coking workshop, which is extremely unfavorable to the optimal control of desulfurization and denitriation process. An intelligent integrated prediction model of flue gas SO2 concentration,...
Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been...
This paper presents the application of an artificial neural network to perform an analysis of the Coefficient of Performance for a compression vapor system operating with R1234yf. A testing facility was built to measure several parameters at the input and at the output of the refrigeration system. These parameters were: the compressor rotation speed, the temperature, and the volumetric flow in the...
Accurate forecasting of solar power is needed for the successful integration of solar energy into the electricity grid. In this paper we consider the task of predicting the half-hourly solar photovoltaic power for the next day from previous solar power and weather data. We propose and evaluate several clustering based methods, that group the days based on the weather characteristics and then build...
Industry complex include various facilities. Pipelines out of facilities are very important because they carry out source materials. Especially, a gas leak of pipeline manages carefully owing to spread other situation such as fire and explosion. Although various researches have been studied in the convergence between computer science and chemical engineering, they need a number of input variables...
An overview of nonlinear predictive control based on neural and fuzzy models is given. The similarities and differences of these two modeling approaches are discussed, as well as their advantages and drawbacks. Several optimization approaches within the predictive controller based on these nonlinear model structures are reviewed, including iterative methods, operating-point and feedback linearization,...
This paper presents the development of nonlinear black-box climate models of typical greenhouses in the Mediterranean area. Using data obtained from actuators and climate sensors in real greenhouses, the problem of neural identification is tackled using a static (non-recurrent) neural network in an autoregressive configuration (NARX). The selection of a set of input variables, a set of input/output...
Short-term forecasting of the energy production is one of the key issues in smart homes that tend to achieve efficient balance among the energy production, storage and consumption. In this paper, we first perform an analysis of the features to be used by the most promising short-term forecast model: artificial neural networks. We determine the best performing offline model and then propose an online...
In Vacuum Oxygen Decarburization(VOD) steel refining process, the endpoint carbon content and endpoint temperature are criteria for smelting products. A VOD model is often needed to predict the endpoint data. During the modeling of VOD, some parameters are difficult to chose, thus affects the model prediction accuracy. Based on the VOD mathematical model, the process is analyzed to chose the main...
This paper presents a real-world application of neural networks. This application considers the estimation of the convection heat transfer coefficient of a run-out cooling table in a steel-making process. Firstly, data of several runs were collected considering the cooling table variables and the reached temperatures. Afterwards, using numerical models and optimization, the equivalent heat transfer...
The aim of this study was to develop an intelligent sensor for acquiring temperature, solar radiation data and estimate cloudiness indexes, and use these measured values to predict temperature and solar radiation in a close future. The prototype produced can ultimately be used in systems related to thermal comfort in buildings and to the efficient and intelligent use of solar energy. To incorporate...
A neural network model which predicted plant shoot-tip temperature was constructed on six crops. The model had three layers, as input, middle and output layers, and the inputs of the model were drybulb temperature, wetbulb temperature, glazing temperature and solar radiation in greenhouse, then the output was shoot-tip temperature. The data for training and verification were collected in greenhouse...
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