Smart Strategies and Harnessing Iot And Deep Learning for Sustainable Waste Reuse in Cement Factories

Abstract

Introduction: In this research paper, the innovative application of Deep Learning (DL), especially deep neural network algorithm, is explored to improve the waste management and recycling strategy in cement factories. As important as the cement industry is in building cities, its production is a major contributor to environmental pollution. Large amounts of gaseous waste, slag and kiln dust negatively affect human health and the environment. Traditional waste management strategies lack efficiency and sustainability, which leads to waste of resources and increased landfills.

Objectives: This study aims to build an effective strategy for recycling and improving by-products using an Artificial Intelligence (AI) algorithm for smart management to preserve the environment and predict the best way to work in cement factories. Controlling the feedback weight of neural network derived from the data comes from the Internet of Things (IoT) as the sensors play the key role in enhancing the results.

Methods: Many efforts are being made to automate factories and plants and to protect the environment from their waste. Cement factories are among the most polluting products in the environment. To control these wastes, we propose sensors in waste production sites to read the information and thus process and analyze the information and automate the work in the factory. The method used consists of several stages, starting with collecting information from a standard dataset and preprocessing the data, then extracting the important features and storing them in special vectors. When extracting features, a neural network is designed based on the weights of the features to enter the data in the vectors to the input layer in the neural network and then the hidden layers to produce the result after implementation in the output layer. In the event that the result is not obtained, the design of the hidden layers is changed based on the feedback data, and calculated and classified to contribute to building the neural network in the next cycle.

Results: The results proved in terms of reducing CO2 emissions and reducing RMSE on historical data and achieved the accuracy of 95% that improved the strategy.

Conclusions This study sheds light on the possibility of using artificial intelligence algorithms as tools to drive sustainability in the industry, especially the cement industry, and the future research avenues in this direction