Optimization of the composting process using artificial neural networks—a literature review

Composting is a complex biological process, and due to the numerous variables affecting its course, it requires constant supervision and, depending on the needs, appropriate modifications. In particular, it is necessary to strive to ensure the quality of substrates, the elimination of possible contaminants, the efficient and inexpensive conduct of the process, and the fulfillment by the finished compost of the quality requirements allowing its use as a fertilizer or crop improvement agent. Therefore, new effective methods for composting optimization are needed. This paper reviews the state of the art on the use of artificial neural networks (ANN) in bio-waste composting with a special focus on applying machine learning tools. Artificial neural networks were characterized along with their division into different types, the basics of the composting process and legal requirements for bio-waste recycling were described. Different types of machine learning were compared with attention paid to the effectiveness of the tools used. Also, for further studies, the appropriate independent variables were proposed to be used in ANN designing. The presented examples of the application of ANN confirm the usefulness of this method, to solve the complexity of the composting issue, and the need for further research.