Biohydrogen production by batch indoor and outdoor photo-fermentation with an immobilized consortium: A process model with Neural Networks

Towards industrial biological hydrogen production: a review

Increased production of renewable energy sources is becoming increasingly needed. Amidst other strategies, one promising technology that could help achieve this goal is biological hydrogen production. This technology uses micro-organisms to convert organic matter into hydrogen gas, a clean and versatile fuel that can be used in a wide range of applications. While biohydrogen production is in its early stages, several challenges must be addressed for biological hydrogen production to become a viable commercial solution. From an experimental perspective, the need to improve the efficiency of hydrogen production, the optimization strategy of the microbial consortia, and the reduction in costs associated with the process is still required. From a scale-up perspective, novel strategies (such as modelling and experimental validation) need to be discussed to facilitate this hydrogen production process. Hence, this review considers hydrogen production, not within the framework of a particular production method or technique, but rather outlines the work (bioreactor modes and configurations, modelling, and techno-economic and life cycle assessment) that has been done in the field as a whole. This type of analysis allows for the abstraction of the biohydrogen production technology industrially, giving insights into novel applications, cross-pollination of separate lines of inquiry, and giving a reference point for researchers and industrial developers in the field of biohydrogen production.

Machine learning and circular bioeconomy: Building new resource efficiency from diverse waste streams

Biorefinery systems are playing pivotal roles in the technological support of resource efficiency for circular bioeconomy. Meanwhile, artificial intelligence presents great potential in handling scientific tasks of high-dimensional complexity. This review article scrutinizes the status of machine learning (ML) applications in four critical biorefinery systems (i.e. composting, fermentation, anaerobic digestion, and thermochemical conversions) as well as their advancements against traditional modeling techniques of mechanistic approach. The contents cover their algorithm selections, modeling challenges, and prospective improvements. Perspectives are sketched to further inform collective efforts on crucial aspects. The multidisciplinary interchange of modeling knowledge will enable a more progressive digital transformation of sustainability efforts in supporting sustainable development goals.

Machine learning in fermentative biohydrogen production: Advantages, challenges, and applications

Hydrogen can be produced in an environmentally friendly manner through biological processes using a variety of organic waste and biomass as feedstock. However, the complexity of biological processes limits their predictability and reliability, which hinders the scale-up and dissemination. This article reviews contemporary research and perspectives on the application of machine learning in biohydrogen production technology. Several machine learning algorithems have recently been implemented for modeling the nonlinear and complex relationships among operational and performance parameters in biohydrogen production as well as predicting the process performance and microbial population dynamics. Reinforced machine learning methods exhibited precise state prediction and retrieved the underlying kinetics effectively. Machine-learning based prediction was also improved by using microbial sequencing data as input parameters. Further research on machine learning could be instrumental in designing a process control tool to maintain reliable hydrogen production performance and identify connection between the process performance and the microbial population.

Machine learning technology in biohydrogen production from agriculture waste: Recent advances and future perspectives

Agricultural waste biomass has shown great potential to deliver green energy produced by biochemical and thermochemical conversion processes to mitigate future energy crises. Biohydrogen has become more interested in carbon-free and high-energy dense fuels among different biofuels. However, it is challenging to develop models based on experience or theory for precise predictions due to the complexity of biohydrogen production systems and the limitations of human perception. Recent advancements in machine learning (ML) may open up new possibilities. For this reason, this critical study offers a thorough understanding of ML's use in biohydrogen production. The most recent developments in ML-assisted biohydrogen technologies, including biochemical and thermochemical processes, are examined in depth. This review paper also discusses the prediction of biohydrogen production from agricultural waste. Finally, the techno-economic and scientific obstacles to ML application in agriculture waste biomass-based biohydrogen production are summarized.

A comprehensive review of mathematical models of photo fermentation

This work aims at analyzing and comparing the different modeling approaches used to date to simulate, design and control photo fermentation processes for hydrogen production and/or wastewater treatment. The study is directed to researchers who approach the problem of photo fermentation mathematical modeling. It is a useful tool to address future research in this specific field in order to overcome the difficulty of modeling a complex, not totally elucidate process. We report a preliminary identification of the environmental and biological parameters, included in the models, which affect photo fermentation. Based on model features, we distinguish three different approaches, i.e. kinetic, parametric and non-ideal reactors. We explore the characteristics of each approach, reporting and comparing the obtained results and underlining the differences between models, together with the advantages and the limitations of each of them. The analysis of the approaches indicates that Kinetic models are useful to describe the process from a biochemical point of view, without considering bio-reactor hydrodynamics and the spatial variations that Parametric Models can be utilized to study the influence and the interactions between the operational conditions. They do not take into account the biochemical process mechanism and the influence of reactor hydrodynamics. Quite the opposite, non-ideal reactors models focus on the reactor configuration. Otherwise, the biochemical description of purple non-sulfur bacteria activities is usually simplified. This review indicates that there still is a lack of models that fully describe photo fermentation processes.

Diagnosis of undesired scenarios in hydrogen production by photo-fermentation

This study presents the use of a machine learning method from the artificial intelligence area, such as the support vector machines, applied to the construction of data-based classification models for diagnosing undesired scenarios in the hydrogen production process by photo-fermentation, which was carried out by an immobilized photo-bacteria consortium. The diagnosis models were constructed with data obtained from simulations run with a mechanistic model of the process and assessed on both modelled and experimental batches. The results revealed a 100% diagnosis performance in those batches where light intensity was below and above an optimum operation range. Nevertheless, 55% diagnosis performance was obtained in modelled batches where pH was away from its optimum operation range, showing that diagnosis model predictions during the first observations of those batches were classified as normal operation and revealing diagnosis delay in pH oscillations. In general, results demonstrate the reliability of classification models to be used in future applications such as the on-line process monitoring to detect and diagnose undesired operating conditions and take corrective actions on time to maintain high hydrogen productivities.