Minimizing the Carbon Footprint With the Use of Zeroing Neural Networks
Abstract
This article describes development and application of the approach of using the zeroing neural network (ZNN) to solve the problems of optimizing the carbon footprint emission on the example of the system approach model. The model described is an integrated optimization problem based on the previously developed by other authors of the model and the method of zeroing neural networks. Optimization problem, which is defined by the objective function that represents the minimization of carbon emissions and limitations, is complex. To solve this problem, an approach based on the use of zeroing neural networks was established. The model is an upgraded version of the original model. In this paper, we investigate the possibility of creating a system to minimize the carbon footprint. The system is built on the principles of production planning, which means there is no need for major changes, such as equipment changes. The system takes into account all the necessary indicators for the successful economic existence of the enterprise. When developing such a system, the balance between financial costs and carbon emissions is very important. Accordingly, the optimization task in this case is complex: minimization of the carbon footprint must correspond to the minimization of the costs of the enterprise.
Copyright information
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
About this article
Publication Date
27 February 2023
Article Doi
eBook ISBN
978-1-80296-960-3
Publisher
European Publisher
Volume
1
Print ISBN (optional)
-
Edition Number
1st Edition
Pages
1-403
Subjects
Hybrid methods, modeling and optimization, complex systems, mathematical models, data mining, computational intelligence
Cite this article as:
Bryukhanova, E., & Antamoshkin, O. (2023). Minimizing the Carbon Footprint With the Use of Zeroing Neural Networks. In P. Stanimorovic, A. A. Stupina, E. Semenkin, & I. V. Kovalev (Eds.), Hybrid Methods of Modeling and Optimization in Complex Systems, vol 1. European Proceedings of Computers and Technology (pp. 160-166). European Publisher. https://doi.org/10.15405/epct.23021.20