AI Medical Compendium Journal:
Bioresource technology

Showing 61 to 70 of 116 articles

Artificial intelligence and machine learning for smart bioprocesses.

Bioresource technology
In recent years, the digital transformation of bioprocesses, which focuses on interconnectivity, online monitoring, process automation, artificial intelligence (AI) and machine learning (ML), and real-time data acquisition, has gained considerable at...

Modeling and optimization of photo-fermentation biohydrogen production from co-substrates basing on response surface methodology and artificial neural network integrated genetic algorithm.

Bioresource technology
The main aim of the present study was to establish a relationship model between bio-hydrogen yield and the key operating parameters affecting photo-fermentation hydrogen production (PFHP) from co-substrates. Central composite design-response surface ...

Big data and machine learning driven bioprocessing - Recent trends and critical analysis.

Bioresource technology
Given the potential of machine learning algorithms in revolutionizing the bioengineering field, this paper examined and summarized the literature related to artificial intelligence (AI) in the bioprocessing field. Natural language processing (NLP) wa...

Artificial intelligence and machine learning approaches in composting process: A review.

Bioresource technology
Studies on developing strategies to predict the stability and performance of the composting process have increased in recent years. Machine learning (ML) has focused on process optimization, prediction of missing data, detection of non-conformities, ...

Machine learning for surrogate process models of bioproduction pathways.

Bioresource technology
Technoeconomic analysis and life-cycle assessment are critical to guiding and prioritizing bench-scale experiments and to evaluating economic and environmental performance of biofuel or biochemical production processes at scale. Traditionally, commer...

Review on machine learning-based bioprocess optimization, monitoring, and control systems.

Bioresource technology
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues ...

Application of machine learning on understanding biomolecule interactions in cellular machinery.

Bioresource technology
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecul...

Opportunities and challenges of machine learning in bioprocesses: Categorization from different perspectives and future direction.

Bioresource technology
Recent advances in machine learning (ML) have revolutionized an extensive range of research and industry fields by successfully addressing intricate problems that cannot be resolved with conventional approaches. However, low interpretability and inco...

Applications of artificial intelligence in anaerobic co-digestion: Recent advances and prospects.

Bioresource technology
Anaerobic co-digestion (AcoD) offers several merits such as better digestibility and process stability while enhancing methane yield due to synergistic effects. Operation of an efficient AcoD system, however, requires full comprehension of important ...

Machine learning and statistical analysis for biomass torrefaction: A review.

Bioresource technology
Torrefaction is a remarkable technology in biomass-to-energy. However, biomass has several disadvantages, including hydrophilic properties, higher moisture, lower heating value, and heterogeneous properties. Many conventional approaches, such as kine...