THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING PROCESSES IN ALGAL TECHNOLOGY: FUTURE CHARACTERISTICS AND OPPORTUNITIES

Many machine learning algorithms have been widely used in the detection and classification of microalgae, such as support vector machine, random forest, and neural network. Deep learning technology represented by convolutional neural network is most widely utilized due to its perfect recognition effect on small targets. The efficiency of sampling method can be achieved with the use of advanced Machine Learning framework in microalgae analysis. This helps to reduce the optimal number of experiments required to achieve pick conditions. Deep learning scheme has also been proposed for identifying microalgae species and growth cycles. Natural experiments with this model achieved 97% accuracy. It provides a good technical backing for monitoring the growth process of microalgae and analyzing their directional movements. Algae have huge potential for humans with limitless applications in almost every sector from power generation to food. Algae have a profitable global market with its versatile product range targeting various market segments with enormous demand for high value commodities such as cosmetics, energy, healthcare and food additive sectors. The use of artificial intelligence and machine learning helps to circumvent the labourous processes of staining, labeling and destruction of microalgae growth environment. Algae have great potential to solve the problem of the present energy crisis in the world. They serve as promising raw materials for cosmetics, pharmaceutics and the food industries. Despite these numerous benefits, many challenges persist for large-scale production of microalgae. These involve contamination and evaporation as is the case with open pond culture, slow algal growth rate as well as low biomass yield and huge cost of operation with respect to nutrients supply. Also, harvesting, dewatering, extraction and downstream processing cost are other areas of serious concerns. Algal strain development is undergoing transformation with advances in biotechnology especially in areas of genetic and molecular engineering. Rapid doubling time, high lipid content, water purification, high tolerance to toxic wastewater systems and so on, are notablecharacters for future research.

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Publication Details

Journal
Journal of Advanced Science and Optimization Research
Published
2025-06-11
DOI
https://doi.org/10.70382/sjasor.v8i9.024
Citations
1
Primary Topic
Fault Detection and Control Systems
Type
article
Field-Weighted Citation Impact
1.21
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article

THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING PROCESSES IN ALGAL TECHNOLOGY: FUTURE CHARACTERISTICS AND OPPORTUNITIES

Morenike Omotayo Adeola, I.V CHUKUKA, O.F ONORIASAKPOBARE, I.F ONIANWAH
1 citations
Journal of Advanced Science and Optimization Research
Fault Detection and Control Systems
1.21
article

THE APPLICATION OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN OPTIMIZING PROCESSES IN ALGAL TECHNOLOGY: FUTURE CHARACTERISTICS AND OPPORTUNITIES

Morenike Omotayo Adeola, I.V CHUKUKA, O.F ONORIASAKPOBARE, I.F ONIANWAH
article en
1 citations

Abstract

Many machine learning algorithms have been widely used in the detection and classification of microalgae, such as support vector machine, random forest, and neural network. Deep learning technology represented by convolutional neural network is most widely utilized due to its perfect recognition effect on small targets. The efficiency of sampling method can be achieved with the use of advanced Machine Learning framework in microalgae analysis. This helps to reduce the optimal number of experiments required to achieve pick conditions. Deep learning scheme has also been proposed for identifying microalgae species and growth cycles. Natural experiments with this model achieved 97% accuracy. It provides a good technical backing for monitoring the growth process of microalgae and analyzing their directional movements. Algae have huge potential for humans with limitless applications in almost every sector from power generation to food. Algae have a profitable global market with its versatile product range targeting various market segments with enormous demand for high value commodities such as cosmetics, energy, healthcare and food additive sectors. The use of artificial intelligence and machine learning helps to circumvent the labourous processes of staining, labeling and destruction of microalgae growth environment. Algae have great potential to solve the problem of the present energy crisis in the world. They serve as promising raw materials for cosmetics, pharmaceutics and the food industries. Despite these numerous benefits, many challenges persist for large-scale production of microalgae. These involve contamination and evaporation as is the case with open pond culture, slow algal growth rate as well as low biomass yield and huge cost of operation with respect to nutrients supply. Also, harvesting, dewatering, extraction and downstream processing cost are other areas of serious concerns. Algal strain development is undergoing transformation with advances in biotechnology especially in areas of genetic and molecular engineering. Rapid doubling time, high lipid content, water purification, high tolerance to toxic wastewater systems and so on, are notablecharacters for future research.

Journal of Advanced Science and Optimization Research
Western Delta University (NG)
Openalex Percentile: Top 20%
Fault Detection and Control Systems
1.21
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