Fault diagnosis of a benchmark fermentation process: a comparative study of feature extraction and classification techniques

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 review on the modernization of pharmaceutical development and manufacturing - Trends, perspectives, and the role of mathematical modeling

Recently, the pharmaceutical industry has been facing several challenges associated to the use of outdated development and manufacturing technologies. The return on investment on research and development has been shrinking, and, at the same time, an alarming number of shortages and recalls for quality concerns has been registered. The pharmaceutical industry has been responding to these issues through a technological modernization of development and manufacturing, under the support of initiatives and activities such as quality-by-design (QbD), process analytical technology, and pharmaceutical emerging technology. In this review, we analyze this modernization trend, with emphasis on the role that mathematical modeling plays within it. We begin by outlining the main socio-economic trends of the pharmaceutical industry, and by highlighting the life-cycle stages of a pharmaceutical product in which technological modernization can help both achieve consistently high product quality and increase return on investment. Then, we review the historical evolution of the pharmaceutical regulatory framework, and we discuss the current state of implementation and future trends of QbD. The pharmaceutical emerging technology is reviewed afterwards, and a discussion on the evolution of QbD into the more effective quality-by-control (QbC) paradigm is presented. Further, we illustrate how mathematical modeling can support the implementation of QbD and QbC across all stages of the pharmaceutical life-cycle. In this respect, we review academic and industrial applications demonstrating the impact of mathematical modeling on three key activities within pharmaceutical development and manufacturing, namely design space description, process monitoring, and active process control. Finally, we discuss some future research opportunities on the use of mathematical modeling in industrial pharmaceutical environments.

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.