{"title":"用于光生物反应器性能和生物量预测的降阶混合模型","authors":"Shabnam Shahhoseyni, Lara Greco, Abhishek Sivaram, Seyed Soheil Mansouri","doi":"10.1016/j.algal.2024.103750","DOIUrl":null,"url":null,"abstract":"<div><div>This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.</div></div>","PeriodicalId":7855,"journal":{"name":"Algal Research-Biomass Biofuels and Bioproducts","volume":"84 ","pages":"Article 103750"},"PeriodicalIF":4.6000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A reduced-order hybrid model for photobioreactor performance and biomass prediction\",\"authors\":\"Shabnam Shahhoseyni, Lara Greco, Abhishek Sivaram, Seyed Soheil Mansouri\",\"doi\":\"10.1016/j.algal.2024.103750\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.</div></div>\",\"PeriodicalId\":7855,\"journal\":{\"name\":\"Algal Research-Biomass Biofuels and Bioproducts\",\"volume\":\"84 \",\"pages\":\"Article 103750\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algal Research-Biomass Biofuels and Bioproducts\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S221192642400362X\",\"RegionNum\":2,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOTECHNOLOGY & APPLIED MICROBIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algal Research-Biomass Biofuels and Bioproducts","FirstCategoryId":"99","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S221192642400362X","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
A reduced-order hybrid model for photobioreactor performance and biomass prediction
This paper introduces a hybrid approach for photobioreactor modeling tailored to microalgae cultivation, combining data-driven and mechanistic concepts to improve modeling efficiency and practicality for industrial scale-up applications. Most growth models for microalgae are nonlinear and require experimental measurement of several parameters. The aim of this work is to develop linear practical models for monitoring purposes. A model based on linear coefficients and polynomial features is proposed, balancing interpretability with non-linear representation focusing on model transparency. To simplify the growth model, Taylor series expansion is applied to the Monod and logistic population models. Two scale-specific models are developed and evaluated, offering practical solutions for monitoring microalgae growth in photobioreactors. Therefore, this reduced order representation allows the biomass growth rate to be dependent directly on the biomass concentration. These models do not require exhaustive data collection of substrate concentration over time, making them cost-effective and efficient for industrial applications. This work provides a step forward in photobioreactor modeling, contributing to the sustainable production of microalgae.
期刊介绍:
Algal Research is an international phycology journal covering all areas of emerging technologies in algae biology, biomass production, cultivation, harvesting, extraction, bioproducts, biorefinery, engineering, and econometrics. Algae is defined to include cyanobacteria, microalgae, and protists and symbionts of interest in biotechnology. The journal publishes original research and reviews for the following scope: algal biology, including but not exclusive to: phylogeny, biodiversity, molecular traits, metabolic regulation, and genetic engineering, algal cultivation, e.g. phototrophic systems, heterotrophic systems, and mixotrophic systems, algal harvesting and extraction systems, biotechnology to convert algal biomass and components into biofuels and bioproducts, e.g., nutraceuticals, pharmaceuticals, animal feed, plastics, etc. algal products and their economic assessment