# A Perspective on Emerging Inter-Disciplinary Solutions for the Sustainable Management of Food Waste
## 4 Food Waste Management, Regulations, and Biological Treatment Technologies
### Initiatives Founded on Artificial Intelligence and Machine Learning  
In artificial intelligence and machine learning, neural networks have widely recognized patterns and delivered predictions based on previous experiences and acquired information [148]. The deployment of a neural network model enables data projection that could support the development of a machine learning algorithm to provide a more accurate estimation of results for biogas generation. Elsewhere, [PERSON] et al. [52] successfully used artificial neural networks (ANNs) to simulate an anaerobic fermentation process for biogas creation in combination with wastewater purification in a modern wastewater treatment plant. This was accomplished in a contemporary wastewater treatment facility. The study illustrates the critical need to integrate artificial intelligence with bioprocess engineering. Furthermore, when modelling and optimizing biogas production, artificial neural networks may be used with genetic algorithms (GA) to address complex bioengineering challenges [119]. Of interest to this inter/multidisciplinary perspective, many studies have been conducted on the potential of artificial neural networks (ANNs) in the field of anaerobic digestion [149, 150, 151, 152, 153, 154, 155, 156], and they have been able to predict the biogas output with a good correlation coefficient. Others [157, 158, 159] have also used Fuzzy Logic systems to predict wastewater treatment, anaerobic digestion, and biogas production. Meanwhile, [PERSON] et al. [52] improved biogas production efficiency by applying a mathematical model that took into account the layout of a classic anaerobic digestion system and its two adaptations--two-phase anaerobic digestion (TPAD) and autogenerative high-pressure digestion (AHPD). Here, TPAD raised total biogas output from 9.06 to 9.59%. This is another example demonstrating the value of mathematical models in optimizing anaerobic digestion systems. Many other studies [160, 161, 162] also demonstrated the use of mathematical modelling of anaerobic digestion processes for biogas production under a variety of digester settings. Optimizing full-scale biogas plant operation is critical to make biomass a viable renewable energy source. Among the most often implemented optimization methods is the Nonlinear Model Predictive Control (NMPC). According to [PERSON] et al. [163], so as to predict biogas production in a full-scale biogas plant, and allow for optimal control and operating decisions based on the actual state of the plant, its operation necessitates an online estimation of its operating state, e.g., anaerobic digestion model No.1 (ADM 1). The ability of the scientific community to accurately estimate digestion performance has substantially increased due to the development of mechanistic models such as the ADM 1, according to [PERSON] et al. [44]. For instance, the management of present biological data (e.g., omics) pertaining to the anaerobic digestion process, as well as emerging data that is growing at an accelerating rate, is highly dependent on many other disciplines, of which artificial intelligence and machine learning [164] will play a significant role by combining such data with mathematical models in the near future. However, [PERSON] and [PERSON] [164] argue that to achieve thoughtful fusion and integration of data sources and knowledge competencies, an extensive research effort is required to develop new artificial intelligence-based techniques and community-wide and interdisciplinary collaboration to address several open and fundamental questions regarding food waste management in combination with anaerobic digestion. The discovery of likely functional linkages between genes from single-transcriptomic data using multivariate information demonstrates the feasibility of combining machine learning with mechanistic modelling in cell biology. However, the lack of progress in integrating machine learning and mechanistic modelling techniques in biological research must be addressed at an interdisciplinary level.