用于光生物反应器性能和生物量预测的降阶混合模型

IF 4.6 2区 生物学 Q1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY Algal Research-Biomass Biofuels and Bioproducts Pub Date : 2024-10-18 DOI:10.1016/j.algal.2024.103750
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摘要

本文介绍了一种针对微藻培养的光生物反应器建模混合方法,结合了数据驱动和机理概念,以提高建模效率和工业放大应用的实用性。大多数微藻生长模型都是非线性的,需要对多个参数进行实验测量。这项工作的目的是开发用于监测目的的线性实用模型。我们提出了一个基于线性系数和多项式特征的模型,在可解释性和非线性表示之间取得平衡,重点关注模型的透明度。为了简化增长模型,对 Monod 和 logistic 种群模型采用了泰勒级数展开。开发并评估了两个特定规模的模型,为监测光生物反应器中的微藻生长提供了实用的解决方案。因此,这种降阶表示法允许生物量增长率直接取决于生物量浓度。这些模型不需要收集基质浓度随时间变化的详尽数据,因此在工业应用中具有成本效益和效率。这项工作在光生物反应器建模方面向前迈进了一步,有助于微藻的可持续生产。
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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.
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来源期刊
Algal Research-Biomass Biofuels and Bioproducts
Algal Research-Biomass Biofuels and Bioproducts BIOTECHNOLOGY & APPLIED MICROBIOLOGY-
CiteScore
9.40
自引率
7.80%
发文量
332
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
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