Carbon footprint minimization using zeroing neural networks

Abstract

This article describes the development and application of the approach of using a zeroing neural network (ZNN) to solve problems of optimizing carbon footprint emissions using the example of a system approach model.The described model is an integrated optimization problem based on a model previously developed by other authors and the method of zeroing neural networks. The optimization problem, which is described by the objective function representing the minimization of carbon emissions and restrictions, is complex. To solve this problem, an approach based on the use of zeroing neural networks was developed. The developed model is an improved version of the original model.In this work, we are developing an energyoriented production planning framework that takes into account economic indicators such as demand satisfaction and economies of scale. However, we do not calculate the associated production costs. In fact, it is necessary to find an important compromise between reducing emissions and production costs. Accordingly, energy-oriented production planning can be viewed as a multi-purpose optimization task in which decision makers try to optimize their decisions in terms of a set of goals, such as minimizing total emissions versus minimizing total costs.