Harmful algal blooms (HABs) are a primary environmental concern, threatening freshwater ecosystems and public health and causing economic damages in the billions of dollars annually. These blooms, predominantly driven by phytoplankton species like cyanobacteria, thrive in nutrient-rich, warm, and low-wind environments. Because of the adverse impacts of HABs, this review examines various control methods, focusing on biological strategies as sustainable solutions. While effective in disrupting algal populations, traditional chemical and physical interventions carry ecological risks and can be resource-intensive. Biological control methods, including biomanipulation and using algicidal microorganisms such as Streptococcus thermophiles, Myxobacteria, and Lopharia spadicea, emerge as eco-friendly alternatives offering long-term benefits. Additionally, barley and rice straw application has demonstrated efficacy in curbing HAB growth. These biological approaches work by inhibiting algal proliferation, disrupting cellular structures, and fostering algal cell aggregation. Despite their advantages over conventional methods, biological controls face challenges, including intricate ecological interactions. This article delves into the latest biological techniques aimed at eradicating HABs, intending to diminish their frequency and reduce toxin levels in aquatic environments. While most research to date has been confined to laboratory settings, scaling these methods to field applications presents hurdles due to the variability and complexity of natural ecosystems. The review underscores the need for further research and development in this critical area of environmental science.

Demand for clean energy has increased due to the proliferation of climate change impact from excessive emission of greenhouse gases (GHG) from the combustion of fossil fuels. H₂ is a clean energy source since water vapor is the only byproduct after its combustion. Growing microalgae offers a promising low-energy and low-cost approach for bioH₂ production. In this study, a bibliometric analysis was performed for the production of H₂ using microalgae to evaluate the conceptual, intellectual, and social structure of the dataset. In addition, a scoping review of articles was conducted to highlight recent advancements and identify future research recommendations. A total of 184 relevant publications over 23 years (2000–2022) were retrieved from the Scopus database for analysis. The results demonstrated an exponential increase in citations from 283 to 996 in the last decade, indicating the interest in bioH₂ production from microalgae. Results also revealed that the International Journal of Hydrogen Energy accounted for more than 25% of the published articles, of which China contributed almost 28%. Oxygen sensitivity of the H₂ase enzyme and sulfur deprivation were highlighted as the main limiting factors of bioH₂ production using microalgae. It was also evident that the most widely studied microalgae species were green algae, especially Chlamydomonas and Chlorella. Effective process modifications, particularly hybridizing microalgae with bacteria consortium and implementing oxygen regulating strategies, were shown to give up to a 10-fold increase in H₂ yield. This study also discusses recent developments in technologies, strategies, microalgal species, and optimizing controlling factors affecting bioH₂ production.

An artificial neural network (ANN) approach with response surface methodology (RSM) technique has been applied to model and optimize the removal process of Brilliant Green dye by batch electrocoagulation process. A multilayer perceptron (MLP) - ANN model has been trained by four input neurons which represent the reaction time, current density, pH, NaCl concentration, and two output neurons representing the dye removal efficiency (%) and electrical energy consumption (kWh/kg). The optimized hidden layer neurons were obtained based on a minimum mean squared error. The batch electrocoagulation process was optimized using central composite design with RSM once the ANN network was trained and primed to anticipate the output. At optimized condition (electrolysis time 10 min, current density 80 A/m², initial pH 5 and electrolyte NaCl concentration 0.5 g/L), RSM projected decolorization of 98.83% and electrical energy consumption of 14.99 kWh/kg. This study shows that the removal of brilliant green dye can be successfully carried out by a batch electrocoagulation process. Therefore, the process is successfully trained by ANN and optimized by RSM for similar applications.