Knowledge culture conditioned bounded rationality and human artificial neural network processes (HANNP decision theory): How risks of accidents and environmental impact of a new chemical production process and plant site have entered decisions

2009 ◽  
Vol 47 (6) ◽  
pp. 843-852 ◽  
Author(s):  
Christoph de Haën
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Environmental Impact ◽  
Decision Theory ◽  
Production Process ◽  
Bounded Rationality ◽  
Chemical Production ◽  
Knowledge Culture ◽  
Plant Site ◽  
Artificial Neural

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

scholarly journals Model for Predicting the Machinability of Continuously Cast and Subsequently Rolled Steel Using the Artificial Neural Network

2021 ◽  
Vol 15 (3) ◽  
pp. 381-386
Author(s):  
Miha Kovačič ◽  
Shpetim Salihu ◽  
Uroš Župerl
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
Chemical Elements ◽  
Cast Steel ◽  
Model Development ◽  
Cutting Speed ◽  
Steel Production ◽  
Continuously Cast ◽  
Artificial Neural

The paper presents a model for predicting the machinability of steels using the method of artificial neural networks. The model includes all indicators from the entire steel production process that best predict the machinability of continuously cast steel. Data for model development were obtained from two years of serial production of 26 steel grades from 255 batches and include seven parameters from secondary metallurgy, four parameters from the casting process, and the content of ten chemical elements. The machinability was determined based on ISO 3685, which defines the machinability of a batch as the cutting speed with a cutting tool life of 15 minutes. An artificial neural network is used to predict this cutting speed. Based on the modelling results, the steel production process was optimised. Over a 5-month period, an additional 39 batches of 20MnV6 steel were produced to verify the developed model.

scholarly journals STATISTICAL SIGNIFICANCE OF PREDICTING THE PRODUCTION PROCESS RESULTS BY MEANS OF AN ARTIFICIAL NEURAL NETWORK

2018 ◽  
Vol 3 (3) ◽  
pp. 117-123
Author(s):  
Александр Макаров ◽  
Aleksandr Makarov
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Lower Bound ◽  
Confidence Interval ◽  
Production Process ◽  
Statistical Significance ◽  
Test Sample ◽  
Pac Learning ◽  
Sample Complexity ◽  
Artificial Neural

As a result of the research of production process organization for the roof construction of residential multi-storey buildings, an artificial neural network (ANN) was designed, the purpose of which is to predict the labor productivity based on organizational factors. One of the main tasks on the way to this purpose is the training of ANN on precedents of the sample extracted from the research object. In view of the deficiency of training data, the main problem is to determine the conditions for the statistical significance of the predictions of the model trained on limited sample. This article is devoted to solving this problem within the research of production organization. The paper uses the provisions of the statistical learning theory, the notion of the Vapnik-Chervonenkis dimension for describing the sample complexity, and also the approaches of probably approximately correct learning (PAC-learning). The technologies of statistical bootstrapping and bagging are described, which allow expanding the training sample. ANN training is conducted using a computer experiment on the programming language Python. The bounds of the theoretical sample complexity, which is necessary for obtaining of ANN results within a given confidence interval with a confidence level of 0,95, were estimated. The sample was transformed by an order comparable to the theoretical lower bound. ANN was trained and the mean square error (MSE) in the test sample was defined, which amounted to . The theoretical bounds of the sample complexity to ensure a given statistical significance are determined in the article. After the ANN training on the sample, the order of which corresponds to theoretical lower bound, a prediction error was obtained on the test sample within the given confidence interval.

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