Artificial intelligence and machine learning for smart bioprocesses

Bioresource Technology, Год журнала: 2023, Номер 375, С. 128826 - 128826

Опубликована: Март 5, 2023

Язык: Английский

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The application of magical microalgae in carbon sequestration and emission reduction: Removal mechanisms and potential analysis

Renewable and Sustainable Energy Reviews, Год журнала: 2024, Номер 197, С. 114417 - 114417

Опубликована: Апрель 1, 2024

Язык: Английский

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Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Bioengineered, Год журнала: 2023, Номер 14(1)

Опубликована: Авг. 14, 2023

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long time, low yield production. To date, artificial intelligence (AI) models can assist improvise bottleneck fucoxanthin process by establishing new processes which involve big data, digitalization, automation efficiency This review highlights application AI such as neural network (ANN) adaptive neuro fuzzy inference system (ANFIS), capable learning patterns relationships from large datasets, capturing non-linearity, predicting optimal conditions significantly impact yield. On top that, combining metaheuristic algorithm genetic (GA) further improve parameter space discovery ANN ANFIS models, results R2 accuracy ranging 98.28% to 99.60% after optimization. Besides, support vector machine (SVM), convolutional networks (CNNs), have been leveraged fucoxanthin, either computer vision based on color images or regression analysis statistical data. The findings reliable when modeling concentration pigments with 66.0% − 99.2%. paper has reviewed feasibility potential purposes, reduce cost, accelerate yields, development fucoxanthin-based products.

Язык: Английский

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An artificial intelligence approach for identification of microalgae cultures

New Biotechnology, Год журнала: 2023, Номер 77, С. 58 - 67

Опубликована: Июль 17, 2023

In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The is essential to guarantee quality biomass, and objective work achieve simple fast method address issue. Data acquisition was performed using FlowCam, device capable capturing images cells detected in culture sample, which are used as inputs by model. can distinguish between 6 different genera microalgae, having trained with several species each genus. It further complemented classification threshold discard unwanted objects while improving overall accuracy achieved an up 97.27% when classifying culture. results demonstrate effectiveness Deep Learning models cultures, it being useful tool monitoring large-scale production facilities providing accurate over wide range genera.

Язык: Английский

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Artificial intelligence-driven microalgae autotrophic batch cultivation: A comparative study of machine and deep learning-based image classification models

Algal Research, Год журнала: 2024, Номер 79, С. 103400 - 103400

Опубликована: Фев. 8, 2024

Язык: Английский

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Algal–bacterial bioremediation of cyanide-containing wastewater in a continuous stirred photobioreactor

World Journal of Microbiology and Biotechnology, Год журнала: 2025, Номер 41(2)

Опубликована: Янв. 9, 2025

Abstract This study reports the isolation and characterization of highly resistant bacterial microalgal strains from an Egyptian wastewater treatment station to cyanide-containing compounds. The strain was identified as Bacillus licheniformis by 16S rRNA gene sequencing. isolate removed up 1 g L −1 potassium cyanide, 3 benzonitrile, sodium salicylate when incubated 10% v/v in MSM at 30 ℃. However, it failed degrade thiocyanate all tested concentrations. electron microscopy a Chlorella spp .. Algal toxicity incubating microalgae 6% containing 2 − NaHCO with increasing concentrations pollutants. Results showed that 0.05 KCN, 1.5 5 KSCN, inhibited 93%, 96%, 75%, 21% algal growth, respectively. In continuous stirred photobioreactor, bacterial-microalgal microcosm detoxified synthetic 0.2 0.1 0.5 3.5 days hydraulic retention time. System failure recorded KCN concentration increased 0.25 . effluent had no inhibitory effect on germination Lepidium sativum seeds phytotoxicity testing. Temperature, pH, chitosan effects were assessed algal/bacterial settleability. Statistical analysis significant difference between parameters. represents potential candidate for industrial cyanide

Язык: Английский

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The need for smart microalgal bioprospecting

Natural Products and Bioprospecting, Год журнала: 2025, Номер 15(1)

Опубликована: Янв. 15, 2025

Abstract Microalgae’s adaptability and resilience to Earth’s diverse environments have evolved these photosynthetic microorganisms into a biotechnological source of industrially relevant physiological functions biometabolites. Despite this, microalgae-based industries only exploit handful species. This lack biodiversity hinders the expansion microalgal industry. Microalgal bioprospecting, searching for novel biological algal resources with new properties, remains low throughput time-consuming endeavour due inefficient workflows that rely on non-selective sampling, monoalgal culture status outdated, non-standardized characterization techniques. review will highlight importance bioprospecting critically explore commonly employed methodologies. We also current advances driving next generation smart focusing transdisciplinary methodologies potential enable high-throughput biodiscoveries. Images adapted from (Addicted04 in Wikipedia File: Australia globe (Australia centered).svg. 2014.; Jin et al. ACS Appl Bio Mater 4:5080–5089, 2021; Kim Microchim Acta 189:88, 2022; Tony Lab Chip 15, 19:3810–3810; Thermo Fisher Scientific INC. CTS Rotea Brochure). Graphical abstract

Язык: Английский

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Harmful Cyanobacterial Blooms: Going beyond the “Green” to Monitor and Predict HCBs

Hydrobiology, Год журнала: 2024, Номер 3(1), С. 11 - 30

Опубликована: Янв. 2, 2024

Under the Climate Change scenario, occurrence of Harmful Cyanobacterial Blooms (HCBs) is an increasingly concerning problem. Particularly for inland freshwaters, that have human populations depending on them consumption or recreation, HCBs can lead to serious ecological damages and socio-economic impacts, but also health risks local communities. From satellite imagery molecular data, there increasing number methodological approaches help improve monitoring prediction cyanobacterial blooms. However, although each methodology has its own strengths limitations, generally a lack data addressing specific intraspecific information, which implications modelling real dynamics toxicity HCBs. The present review intends make quick overview current monitor blooms provide tier-based integrative perspective their application. A transversal at wide scale should be enhanced cannot rely only pigment levels rather include diversity information obtained from modern tools. This crucial achieve effective prediction, management under severity trends in freshwaters.

Язык: Английский

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DAD-YOLO as a novel computer vision tool to predict the environmental impact of harmful algae presence in contaminated river water employed for large-scale irrigation to agricultural field

Journal of Water Process Engineering, Год журнала: 2025, Номер 71, С. 107439 - 107439

Опубликована: Март 1, 2025

Язык: Английский

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AI-Driven Algae Biorefineries: A New Era for Sustainable Bioeconomy

Current Pollution Reports, Год журнала: 2025, Номер 11(1)

Опубликована: Апрель 11, 2025

Язык: Английский

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