A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning
المؤلفون المشاركون
Wu, Jing
Cheng, Yanming
Liu, Cheng
Lee, Ilkyoo
Huang, Wenlin
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-10، 10ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-05-15
دولة النشر
مصر
عدد الصفحات
10
التخصصات الرئيسية
الملخص EN
In this paper, achieving minimum energy consumption in the copper electrowinning process is taken as the research objective.
In the traditional production process, sulfate ion concentration, copper ion concentration, and current density are carried out according to the empirical value, which cannot ensure the energy consumption reaching the optimal level.
Therefore, this paper proposes a BP neural network model to optimize energy consumption according to the relationship between current density, sulfate ion concentration, copper ion concentration, electrolytic tank voltage, and current efficiency, and the established BP neural network model is trained by using real data from the enterprise.
The simulation results show that there is a definite error between the predicted electrolytic tank voltage and current efficiency and corresponding to predict electrolytic tank voltage and current efficiency measured at the production site.
The BP neural network improved by GA is proposed to further improve the prediction accuracy of the BP neural network.
Simulation results indicate that the prediction error of electrolytic tank voltage and current efficiency is greatly reduced that meets the accuracy requirements, and then the minimum energy consumption can be calculated.
On the premise of guaranteeing the quality of copper electrowinning, the current density, sulfate ion concentration, and copper ion concentration corresponding to the minimum energy consumption accurately predicted by this method can be respectively adjusted in real time, which realizes the optimization of energy consumption in the process of copper electrowinning under the background of low carbon and environmental protection.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wu, Jing& Cheng, Yanming& Liu, Cheng& Lee, Ilkyoo& Huang, Wenlin. 2020. A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1192993
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wu, Jing…[et al.]. A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1192993
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wu, Jing& Cheng, Yanming& Liu, Cheng& Lee, Ilkyoo& Huang, Wenlin. A BP Neural Network Based on GA for Optimizing Energy Consumption of Copper Electrowinning. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1192993
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1192993
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر