Kuang Zhu, Wenjuan Zhang, Elchin Jafarov, Satish Karra, Kurt Solander, Meltem Urgun Demirtas, Lutgarde Raskin, Steven Skerlos
{"title":"Open-Source Anaerobic Digestion Modeling Platform, Anaerobic Digestion Model No. 1 Fast (ADM1F)","authors":"Kuang Zhu, Wenjuan Zhang, Elchin Jafarov, Satish Karra, Kurt Solander, Meltem Urgun Demirtas, Lutgarde Raskin, Steven Skerlos","doi":"10.1002/bit.28906","DOIUrl":null,"url":null,"abstract":"An open-source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open-source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady-state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two-phase co-digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.","PeriodicalId":9168,"journal":{"name":"Biotechnology and Bioengineering","volume":"89 1","pages":""},"PeriodicalIF":3.5000,"publicationDate":"2024-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biotechnology and Bioengineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1002/bit.28906","RegionNum":2,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
An open-source modeling platform, called Anaerobic Digestion Model No. 1 Fast (ADM1F), is introduced to achieve fast and numerically stable simulations of anaerobic digestion processes. ADM1F is compatible with an iPython interface to facilitate model configuration, simulation, data analysis, and visualization. Faster simulations and more stable results are accomplished by implementing an advanced open-source library of numerical methods called Portable Extensive Toolkit for Scientific Computation (PETSc) to solve the ADM1 system of equations. Leveraging PETSc, ADM1F can consistently complete a steady-state simulation under 0.2 s, over 99% faster than a benchmark ADM1 model implemented with MATLAB while achieving agreement of model outputs within 1% of those obtained with the benchmark model. For dynamic simulations, however, ADM1F has a computational speed advantage only when the influent characteristics update more frequently than every 4 h. The ability of ADM1F to be useful as a tool to study anaerobic digestion systems is demonstrated through two example implementations of ADM1F: (1) a two-phase co-digestion scenario evaluating the impact of the organic loading rate and the substrate composition on reactor performance and stability, and (2) a conventional digester scenario assessing the effectiveness of recovery strategies after disruptions that led to instability. These examples demonstrate how the high simulation speed and the convenience of the iPython interface allow ADM1F to complete complex analyses within minutes, much faster than computational strategies currently reported in the literature.
期刊介绍:
Biotechnology & Bioengineering publishes Perspectives, Articles, Reviews, Mini-Reviews, and Communications to the Editor that embrace all aspects of biotechnology. These include:
-Enzyme systems and their applications, including enzyme reactors, purification, and applied aspects of protein engineering
-Animal-cell biotechnology, including media development
-Applied aspects of cellular physiology, metabolism, and energetics
-Biocatalysis and applied enzymology, including enzyme reactors, protein engineering, and nanobiotechnology
-Biothermodynamics
-Biofuels, including biomass and renewable resource engineering
-Biomaterials, including delivery systems and materials for tissue engineering
-Bioprocess engineering, including kinetics and modeling of biological systems, transport phenomena in bioreactors, bioreactor design, monitoring, and control
-Biosensors and instrumentation
-Computational and systems biology, including bioinformatics and genomic/proteomic studies
-Environmental biotechnology, including biofilms, algal systems, and bioremediation
-Metabolic and cellular engineering
-Plant-cell biotechnology
-Spectroscopic and other analytical techniques for biotechnological applications
-Synthetic biology
-Tissue engineering, stem-cell bioengineering, regenerative medicine, gene therapy and delivery systems
The editors will consider papers for publication based on novelty, their immediate or future impact on biotechnological processes, and their contribution to the advancement of biochemical engineering science. Submission of papers dealing with routine aspects of bioprocessing, description of established equipment, and routine applications of established methodologies (e.g., control strategies, modeling, experimental methods) is discouraged. Theoretical papers will be judged based on the novelty of the approach and their potential impact, or on their novel capability to predict and elucidate experimental observations.