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Bio-based plastics, primarily polyhydroxyalkanoates (PHAs), offer a hopeful alternative to petroleum-derived plastics. Third-generation (3G; microalgae/cyanobacteria) biomass has gained significant importance due to its rapid biomass productivity and metabolic versatility. Microalgae can produce PHAs by utilizing CO2 and wastewater, establishing them as highly promising and eco-friendly systems for bioplastic production. This comprehensive review presents comprehensive insights into microalgae-PHA production, from optimization of physicochemical and cultural conditions to effective PHA purification processes. The critical review also examines the latest advancements in cultivation strategies, metabolic engineering, and bioreactor developments, which may lead to more sustainable and progressive microalgal-based bioplastic accumulation. The effectiveness of algae biomass generation for PHA accumulation through integrated wastewater treatment has been addressed. This review examines the role of mathematical modeling and emerging artificial intelligence in advancing algae-based PHA production processes. Finally, the review concludes with a discussion of the economic and social challenges, life cycle analysis, and prospects for research and development of advanced microalgal-derived bioplastics production and predictions of potential solutions for economically feasible and sustainable microalgae-based PHA production at the industrial scale.

Agriculture provides basic livelihood for a large section of world's population. It is the oldest economic activity in India, with two third of Indian population involved in crop production. India is second largest producer of rice and biggest exporter globally, with rice which is most common staple crop consumed in country. However, there are several challenges for paddy production including small production yield, soil quality, seed quality, huge volume of water needed and biotic stress. Of these, biotic stress drastically affects yield and susceptibility to other diseases in paddy production. It is caused by pathogens such as bacteria, viruses, fungi, nematodes, all of which severely affect growth and productivity of paddy crop. To mitigate these challenges, infected crops are identified, detected, classified, categorized, and prevented according to their respective suffering disease by using conventional methods which are not effective and efficient for growth of paddy crop. Thus, use of artificial intelligence (AI) and a smart agriculture-based Internet of Things (IoT) platform could be effective for detecting the biotic stresses in very less time or online mode. For this, deep learning, and convolutional neural networks (CNN) multi-structured layer approach were used for diagnosing disease in rice plants. Different models and classifiers of CNN were used for detecting disease by processing high-spectral images and using logistic and mathematical formulation methods for classification of biotic paddy crop stresses. Continuous monitoring of stages of infection in paddy crop can be achieved using real-time data. Thus, use of AI has made diagnosing paddy crop diseases much easier and more efficient.

In the pursuit of sustainable solutions for contemporary environmental challenges arising from the increasing global demand for energy, this study delves into the potential of cyanobacteria, specifically Arthrospira platensis (commonly known as “spirulina”), as a versatile resource. Employing a life cycle assessment (LCA) in accordance with the ISO 14044:2006 standard and employing both midpoint and endpoint indicators, the study comprehensively evaluates environmental impacts. The research explored a range of scenarios, specifically investigating variations in light intensity and harvesting volume. These investigations were carried out using a pilot-scale photobioreactor, specifically an airlift reactor system featuring a horizontal tubular downcomer. The primary focus is on extracting valuable compounds, namely exopolysaccharides and phycocyanin. It emphasized the extraction of value-added products and strategic integration with a biogas plant for process heat, contributing to developing a sustainable supply network and offering insights into environmentally conscious algae cultivation practices with implications for renewable energy and the production of valuable products. The results emphasize the project’s potential economic feasibility with minimal energy impact from by-product extraction. The environmental assessment identifies marine ecotoxicity and fossil resource depletion as principal impacts, predominantly influenced by upstreaming and harvesting stages. After conducting comparisons across various scenarios, it was found that cultivations under higher light intensities have a lower environmental impact than cultivations with low light supply. However, regardless of light intensity, processes with shorter harvesting cycles tend to have a smaller environmental impact compared to processes with longer harvesting cycles. Overall, this research contributes a nuanced and realistic perspective, fostering informed decision-making in sustainable algae cultivation practices, with implications for renewable energy and valuable compound production.

Bio-based plastics, primarily polyhydroxyalkanoates (PHAs), offer a hopeful alternative to petroleum-derived plastics. Thirdgeneration (3G; microalgae/cyanobacteria) biomass has gained significant importance due to its rapid biomass productivity and metabolic versatility. Microalgae can produce PHAs by utilizing CO2 and wastewater, establishing them as highly promising and eco-friendly systems for bioplastic production. This comprehensive review presents comprehensive insights into microalgae-PHA production, from optimization of physicochemical and cultural conditions to effective PHA purification processes. The critical review also examines the latest advancements in cultivation strategies, metabolic engineering, and bioreactor developments, which may lead to more sustainable and progressive microalgal-based bioplastic accumulation. The effectiveness of algae biomass generation for PHA accumulation through integrated wastewater treatment has been addressed. This review examines the role of mathematical modeling and emerging artificial intelligence in advancing algaebased PHA production processes. Finally, the review concludes with a discussion of the economic and social challenges, life cycle analysis, and prospects for research and development of advanced microalgal-derived bioplastics production and predictions of potential solutions for economically feasible and sustainable microalgae-based PHA production at the industrial scale. [ABSTRACT FROM AUTHOR]

Cascade systems are used in the large-scale production of astaxanthin, facilitating a successful value-added process despite high accumulating costs. However, their application to other high-value products (HVPs), like lutein, β-carotene, chlorophylls, and fatty acids, remains unexplored. This study investigates Chlamydomonas asymmetica in chemostatic cultures, focusing on the impact of light and dilution rate. A two-stage cascade system is designed, combining high-light growth with low-light pigment accumulation. The results show potential for productivity improvement. Notably, the spacetime yield (STY) of Chlorophyll a increased by 20.96%, reaching 2.73 g·L−1·d−1 at the lowest dilution rate. Lutein maintains a consistent concentration of 22.34 mg·g−1, while β-carotene achieves a maximum STY of 3.60 mg·L·d−1. A cascade modification with a hollow fiber membrane significantly enhances HVP concentrations—Chlorophyll b, Lutein, Chlorophyll a, β-carotene, EPS, and GLA increase 27.23%, 38.95%, 31.88%, 86.19%, 128.7%, and 57.71%, respectively. STY improvements for these HVPs range from 1.78% to 82.96%. This study offers insights into C. asymmetica’s response and proposes a cascade modification for enhanced HVP production and downstream processing efficiency.

Algae are capable of sequestering nutrients such as nitrates and phosphates from wastewater in the presence of sunlight and carbon dioxide (CO2) to build up their body mass and help combat climate change. In the current study, we carried out different case studies to estimate the volume of algal biomass that could be produced annually using the rotating algal biofilm (RAB) method in three large-scale water resource recovery facilities (WRRFs) in Texas: Fort Worth, Dallas, and Houston. We calculated the total amount of lipids, carbohydrates, and proteins that could be fractionated from the algal biomass while using the hydrothermal flash hydrolysis process, followed by converting these biomolecules into commodity products via reported methods and yields. In the first case study, we estimated the amount of biogas and electricity produced in anaerobic digesters when the algal biomass and sludge generated in large-scale WRRFs are co-digested. Using this approach, electricity generation in a large-scale WRRF could be increased by 23% and CO2 emissions could be further reduced when using biogas combustion exhaust gases as a carbon source for the RAB system. In the second case study, it was estimated that 988 MT mixed alcohol or 1144 MT non-isocyanate polyurethane could be produced annually from the protein fraction in the WRRF in Fort Worth, Texas. In the third case study, it was estimated that 702 MT bio-succinic acid or 520 MT bioethanol could be produced annually using the carbohydrate fraction. In the fourth case study, it was estimated that 1040 MT biodiesel or 528 MT biocrude could be produced annually using the lipid fraction. Producing renewable commodity fuels and chemicals using the algal biomass generated in a WRRF will help to displace fossil fuel-derived products, generate new jobs, and benefit the environment.

Gasification technology often requires the use of modeling approaches to incorporate several intermediate reactions in a complex nature. These traditional models are occasionally impractical and often challenging to bring reliable relations between performing parameters. Hence, this study outlined the solutions to overcome the challenges in modeling approaches. The use of machine learning (ML) methods is essential and a promising integration to add intelligent retrieval to traditional modeling approaches of gasification technology. Regarding this, this study charted applied ML-based artificial intelligence in the field of gasification research. This study includes a summary of applied ML algorithms, including neural network, support vector, decision tree, random forest, and gradient boosting, and their performance evaluations for gasification technologies.