{"title":"利用人工神经网络模拟高产生物柴油藻类对咖啡因的降解。","authors":"Dixita Phukan, Vipin Kumar, Wilson Kandulna, Ankur Singh, Saumya Anand, Nishant Pandey","doi":"10.1016/j.biortech.2024.131935","DOIUrl":null,"url":null,"abstract":"<p><p>In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, photoperiods, caffeine, and indole acetic acid (IAA) concentrations. The ANN model, designed with a 4-15-1 multilayer perceptron and trained on 120 data points, achieved high predictive accuracy (R<sup>2</sup> > 0.96) and bias/accuracy factors between 0.95-1.11. Sensitivity analysis identified pH as the most critical factor. IAA enhanced lipid content in Desmodesmus by 91 % in caffeine-containing simulated wastewater. FAME analysis was performed under optimal lipid-producing conditions (10 ppm caffeine, 5 ppm IAA). IAA upregulated key metabolic pathways, increasing secondary metabolites in Desmodesmus and Chlorella. In summary, the modeling results are key for improving system performance, guiding parameter selection to enhance caffeine removal by Desmodesmus. IAA also enhanced resilience and lipid yield, increasing the economic feasibility of caffeine removal and biofuel production.</p>","PeriodicalId":258,"journal":{"name":"Bioresource Technology","volume":" ","pages":"131935"},"PeriodicalIF":9.7000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.\",\"authors\":\"Dixita Phukan, Vipin Kumar, Wilson Kandulna, Ankur Singh, Saumya Anand, Nishant Pandey\",\"doi\":\"10.1016/j.biortech.2024.131935\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, photoperiods, caffeine, and indole acetic acid (IAA) concentrations. The ANN model, designed with a 4-15-1 multilayer perceptron and trained on 120 data points, achieved high predictive accuracy (R<sup>2</sup> > 0.96) and bias/accuracy factors between 0.95-1.11. Sensitivity analysis identified pH as the most critical factor. IAA enhanced lipid content in Desmodesmus by 91 % in caffeine-containing simulated wastewater. FAME analysis was performed under optimal lipid-producing conditions (10 ppm caffeine, 5 ppm IAA). IAA upregulated key metabolic pathways, increasing secondary metabolites in Desmodesmus and Chlorella. In summary, the modeling results are key for improving system performance, guiding parameter selection to enhance caffeine removal by Desmodesmus. IAA also enhanced resilience and lipid yield, increasing the economic feasibility of caffeine removal and biofuel production.</p>\",\"PeriodicalId\":258,\"journal\":{\"name\":\"Bioresource Technology\",\"volume\":\" \",\"pages\":\"131935\"},\"PeriodicalIF\":9.7000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Bioresource Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1016/j.biortech.2024.131935\",\"RegionNum\":1,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/10 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"AGRICULTURAL ENGINEERING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Bioresource Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.biortech.2024.131935","RegionNum":1,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/10 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.
In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, photoperiods, caffeine, and indole acetic acid (IAA) concentrations. The ANN model, designed with a 4-15-1 multilayer perceptron and trained on 120 data points, achieved high predictive accuracy (R2 > 0.96) and bias/accuracy factors between 0.95-1.11. Sensitivity analysis identified pH as the most critical factor. IAA enhanced lipid content in Desmodesmus by 91 % in caffeine-containing simulated wastewater. FAME analysis was performed under optimal lipid-producing conditions (10 ppm caffeine, 5 ppm IAA). IAA upregulated key metabolic pathways, increasing secondary metabolites in Desmodesmus and Chlorella. In summary, the modeling results are key for improving system performance, guiding parameter selection to enhance caffeine removal by Desmodesmus. IAA also enhanced resilience and lipid yield, increasing the economic feasibility of caffeine removal and biofuel production.
期刊介绍:
Bioresource Technology publishes original articles, review articles, case studies, and short communications covering the fundamentals, applications, and management of bioresource technology. The journal seeks to advance and disseminate knowledge across various areas related to biomass, biological waste treatment, bioenergy, biotransformations, bioresource systems analysis, and associated conversion or production technologies.
Topics include:
• Biofuels: liquid and gaseous biofuels production, modeling and economics
• Bioprocesses and bioproducts: biocatalysis and fermentations
• Biomass and feedstocks utilization: bioconversion of agro-industrial residues
• Environmental protection: biological waste treatment
• Thermochemical conversion of biomass: combustion, pyrolysis, gasification, catalysis.