Improving green hydrogen production from Chlorella vulgaris via formic acid-mediated hydrothermal carbonisation and neural network modelling

Synthetic coalification of microalgae through hydrothermal carbonization: strategies for enhanced hydrochar characteristics and technological advancements

This review explores the hydrothermal carbonization (HTC) of microalgae through a comprehensive evaluation of the influence of process parameters on the resultant products. The findings revealed that HTC of microalgae takes place at lower temperatures (170 - 250 °C) compared to lignocellulosic feedstocks, and the resulting hydrochar and hydrolysate have a higher N-content. Additionally, secondary char production varies based on reaction conditions, with yields between 4 % and 35 %. The interaction between carbohydrates and nitrogenous compounds in the hydrolysate at varying reaction severities was discussed, underlining the extent of nitrogen fixation in the hydrochar and total organic C-content of up to 26.8 g L-1. The article also suggests strategies to improve hydrochar properties by assessing different technical strategies and emphasizing future direction research. In summary, this review underscores the potential of microalgal HTC as a sustainable approach for applications in energy and environmental applications via process optimization and technological upgradation.

Advancements of astaxanthin production in Haematococcus pluvialis: Update insight and way forward

The global market demand for natural astaxanthin (AXT) is growing rapidly owing to its potential human health benefits and diverse industry applications, driven by its safety, unique structure, and special function. Currently, the alga Haematococcus pluvialis (alternative name H. lacustris) has been considered as one of the best large-scale producers of natural AXT. However, the industry's further development faces two main challenges: the limited cultivation areas due to light-dependent AXT accumulation and the low AXT yield coupled with high production costs resulting from complex, time-consuming upstream biomass culture and downstream AXT extraction processes. Therefore, it is urgently to develop novel strategies to improve the AXT production in H. pluvialis to meet industrial demands, which makes its commercialization cost-effective. Although several strategies related to screening excellent target strains, optimizing culture condition for high biomass yield, elucidating the AXT biosynthetic pathway, and exploiting effective inducers for high AXT content have been applied to enhance the AXT production in H. pluvialis, there are still some unsolved and easily ignored perspectives. In this review, firstly, we summarize the structure and function of natural AXT focus on those from the algal H. pluvialis. Secondly, the latest findings regarding the AXT biosynthetic pathway including spatiotemporal specificity, transport, esterification, and storage are updated. Thirdly, we systematically assess enhancement strategies on AXT yield. Fourthly, the regulation mechanisms of AXT accumulation under various stresses are discussed. Finally, the integrated and systematic solutions for improving AXT production are proposed. This review not only fills the existing gap about the AXT accumulation, but also points the way forward for AXT production in H. pluvialis.

Machine learning in microalgae biotechnology for sustainable biofuel production: Advancements, applications, and prospects

This review explores the critical role of machine learning (ML) in enhancing microalgae bioprocesses for sustainable biofuel production. It addresses both technical and economic challenges in commercializing microalgal biofuels and examines how ML can optimize various stages, including identification, classification, cultivation, harvesting, drying, and conversion to biofuels. This review also highlights the integration of ML with technologies such as the Internet of Things (IoT) for real-time monitoring and management of bioprocesses. It discusses the adaptability and flexibility of ML in the context of microalgae biotechnology, focusing on diverse algorithms such as Artificial Neural Networks, Support Vector Machines, Decision Trees, and Random Forests, while emphasizing the importance of data collection and preparation. Additionally, current ML applications in microalgae biofuel production are reviewed, including strain selection, growth optimization, system monitoring, and lipid extraction.

Microalgal biorefineries: Advancement in machine learning tools for sustainable biofuel production and value-added products recovery

The microalgae can be converted into biofuels, biochemicals, and bioactive compounds in a biorefinery. Recently, designing and executing more viable and sustainable biofuel production from microalgal biomass is one of the vital challenges in the development of biorefinery. Scalable cultivation of microalgae is mandatory for commercializing and industrializing the biorefinery. The intrinsic complication in cultivation of microalgae is the physiological and operational factors that renders challenging impact to enable a smooth and profitable operation. However, this aim can only be successful via a simulation prospect. Machine learning tools provides advanced approaches for evaluating, predicting, and controlling uncertainties in microalgal biorefinery for sustainable biofuel production. The present review provides a critical evaluation of the most progressing machine learning tools that validate a potential to be employed in microalgal biorefinery. These tools are highly potential for their extensive evaluation on microalgal screening and classification. However, the application of these tools for optimization of microalgal biomass cultivation in industries in order to increase the biomass production, is still in its initial stages. Integrated hybrid machine learning tools can aid the industries to function efficiently with least resources. Some of the challenges, and perspectives of machine learning tools are discussed. Besides, future prospects are also emphasized. Though, most of the research reports on machine learning tools are not appropriate to gather generalized information, standard protocols and strategies must be developed to design generalized machine learning tools. On a whole, this review offers a perspective information about digitalized microalgal exploitation in a microalgal biorefinery.

Gasification of biomass for syngas production: Research update and stoichiometry diagram presentation

Gasification is a thermal process that converts organic materials into syngas, bio-oil, and solid residues. This mini-review provides an update on current research on producing high-quality syngas from biomass via gasification. Specifically, the review highlights the effective valorization of feedstocks, the development of novel catalysts for reforming reactions, the configuration of novel integrated gasification processes with an assisted field, and the proposal of advanced modeling tools, including the use of machine learning strategies for process design and optimization. The review also includes examples of using a stoichiometry diagram to describe biomass gasification. The research efforts in this area are constantly evolving, and this review provides an up-to-date overview of the most recent advances and prospects for future research. The proposed advancements in gasification technology have the potential to significantly contribute to sustainable energy production and reduce greenhouse gas emissions.

Hybrid Prediction-Driven High-Throughput Sustainability Screening for Advancing Waste-to-Dimethyl Ether Valorization

Assessing the prospective climate preservation potential of novel, innovative, but immature chemical production techniques is limited by the high number of process synthesis options and the lack of reliable, high-throughput quantitative sustainability pre-screening methods. This study presents the sequential use of data-driven hybrid prediction (ANN-RSM-DOM) to streamline waste-to-dimethyl ether (DME) upcycling using a set of sustainability criteria. Artificial neural networks (ANNs) are developed to generate in silico waste valorization experimental results and ex-ante model the operating space of biorefineries applying the organic fraction of municipal solid waste (OFMSW) and sewage sludge (SS). Aspen Plus process flowsheeting and ANN simulations are postprocessed using the response surface methodology (RSM) and desirability optimization method (DOM) to improve the in-depth mechanistic understanding of environmental systems and identify the most benign configurations. The hybrid prediction highlights the importance of targeted waste selection based on elemental composition and the need to design waste-specific DME synthesis to improve techno-economic and environmental performances. The developed framework reveals plant configurations with concurrent climate benefits (-1.241 and -2.128 kg CO2-eq (kg DME)-1) and low DME production costs (0.382 and 0.492 € (kg DME)-1) using OFMSW and SS feedstocks. Overall, the multi-scale explorative hybrid prediction facilitates early stage process synthesis, assists in the design of block units with nonlinear characteristics, resolves the simultaneous analysis of qualitative and quantitative variables, and enables the high-throughput sustainability screening of low technological readiness level processes.

Biohydrogen production via green silver nanoparticles synthesized through biomass of Ulva lactuca bloom

Ulva lactuca is a marine green seaweed. Its bloom based biomass accumulated in the İzmir bay and is collected by local authorities. In this investigation, an alternative solution was proposed to utilize the biomass of U. lactuca to produce biohydrogen via green synthesized silver nanoparticles. According to the results, the optimum conditions related to silver nanoparticle production such as pH, temperature, biomass concentration, silver nitrate concentrations, and incubation time were determined to be 11, 25 °C, 10 mg/mL, 4 mM, and 3 days, respectively. Effective conditions for biohydrogen production such as pH, temperature, agitation rate, and sodium borohydride concentration were found to be 7, 50 °C, 250 rpm and 150 mM, respectively. These parameters are also modelled with an artificial neural network. The data presented here provide recommendations for producing biohydrogen from waste algae and helping reduce carbon emissions for better environment and future.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.