# A Perspective on Emerging Inter-Disciplinary Solutions for the Sustainable Management of Food Waste
## 1 Introduction
For example, mathematical models [39] and low-cost machine learning techniques may be used to monitor critical process parameters and then utilize the simulation model to forecast, optimize, and control processes [40]. Continuous monitoring, which artificial intelligence can perform, is perhaps the most crucial component in ensuring that processes are optimized, and quality standards are met [41]. In addition, artificial intelligence can be utilized to evaluate the features of biomass feedstock, the primary quantities of products, and the overall performance of anaerobic digestion systems [42]. Process failure can be attributed to the lack of automated, rapid, and cost-effective monitoring and control (e.g., biomass feedstock, fermenter volume, substrate, biofuel properties, temperature, pH, and pressure controls) in biogas plants [42; 43]. Process modelling is important because it provides a better understanding of the ideal operating conditions by controlling and forecasting a system's behaviour and outcomes [38]. Indeed, various computational methods and machine learning algorithms have been employed to guarantee that anaerobic digestion systems stay relevant in the face of existing and anticipated challenges. On the other hand, more technical interventions are required to develop new modelling concepts while enhancing old techniques or even integrating tools from other areas [39]. Fortunately, mechanistic models such as Anaerobic Digestion Model 1 (ADM1) have considerably enhanced the scientific community's capacity to forecast digestion performance and biogas output [44]. Relevant to this study and also valuable to waste management efforts, artificial intelligence techniques such as artificial neural networks (ANNs) and adaptive neural fuzzy inference systems (ANFIS) are currently put to use in the predictive modelling of wastewater treatment, anaerobic digestion, and biogas synthesis [45; 46; 47]. Despite the tremendous efforts that have been made to optimize food waste management technology, many problems still exist connected to the systems or processes involved. Consequently, there is an urgent need for numerous disciplines to focus on these challenges, which is why the interdisciplinary approach systems are widely recommended and continue to be seen as essential to accomplishing the vision of a circular economy [48]. In the context of anaerobic digestion, scientific data is generated from meta-omics, omics, bioengineering, bioprocessing technology, and nanotechnology. Most findings recommend further process/system optimization for improved anaerobic digestion technologies. To fully explore food waste management in the context of anaerobic digestion processes, the current review provides essential developments made by the disciplines mentioned above, as well as a critical perspective on several promising and plausible multi/interdisciplinary partnerships in dealing with the challenges of food waste management. Very little interdisciplinary research aimed at enhancing anaerobic digestion processes has been conducted to date. However, the few attempts that have been made recognize the significance of interdisciplinarity, which this study advocates. For instance, [PERSON] et al. [49] coupled nanotechnology and bioengineering by creating the novel concept of exploiting surface-state engineering by administering Fe\({}_{3}\)O\({}_{4}\)-engineered nanoparticles to increase biogas production by 234% using wastewater sludge. The authors anticipated that, since small nanoparticles are unstable, they can be designed in a regulated manner, which would result in the greatest increase in biogas production yet observed. In an effort to further deepen the interdisciplinarity between nanotechnology and bioengineering, [PERSON] et al. [16] used magnetite nanoparticles in batch and continuous flow mesophilic anaerobic digestion to increase biogas yield from food waste. They were able to increase the batch and continuous biogas yields by 33% and 8%, respectively.