Review of advanced physical and data‐driven models for dynamic bioprocess simulation: Case study of algae–bacteria consortium wastewater treatment

• Published in: Biotechnology and bioengineering Vol. 116; no. 2; pp. 342 - 353
• Main Authors: Del Rio‐Chanona, Ehecatl Antonio, Cong, Xiaoyan, Bradford, Eric, Zhang, Dongda, Jing, Keju
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.02.2019
• Subjects: Algae; algae–bacteria consortium; artificial neural network; Artificial neural networks; Bacillus subtilis - growth & development; Bacillus subtilis - metabolism; Bacteria; Biological activity; Biological models (mathematics); Chlorella vulgaris - growth & development; Chlorella vulgaris - metabolism; Computer simulation; Consortia; Datasets; Gaussian process; Gaussian processes; kinetic modeling; Mathematical models; Microbial Consortia; Model accuracy; Models, Theoretical; Neural networks; Regression analysis; Regression models; scarce dataset; Waste Water - microbiology; Wastewater treatment; Water Purification - methods; Water treatment; artificial neural network; algae-bacteria consortium; scarce dataset; Gaussian processes; kinetic modeling
• ISSN: 0006-3592; 1097-0290; 1097-0290
• DOI: 10.1002/bit.26881

Microorganism production and remediation processes are of critical importance to the next generation of sustainable industries. Undertaking mathematical treatment of dynamic biosystems operating at any spatial or temporal scale is essential to guarantee their performance and safety. However, constructing physical models remains a challenge due to the extreme complexity of process biological mechanisms. Data‐driven models also encounter severe limitations because datasets from large‐scale bioprocesses are often scarce without complete information and on a restricted operational space. To fill this gap, the current research compares the performance of advanced physical and data‐driven models for dynamic bioprocess simulations subject to incomplete and scarce datasets, which to the best of our knowledge has never been addressed before. In specific, kinetic models were constructed by integrating different classic models, and state‐of‐the‐art hyperparameter selection frameworks were developed to design artificial neural networks and Gaussian process regression models. An algae–bacteria consortium wastewater treatment process was selected to test the accuracy of these modeling strategies, as it is one of the most sophisticated biosystems due to the intricate mutualistic and competitive interactions. Based on the current results and available data, a heuristic model selection procedure is provided. This study paves the way to facilitate future bioprocess modeling. Undertaking mathematical modelling and optimization of algae‐bacteria consortium wastewater treatment process is essential to guarantee its efficiency, but remains a challenge due to its complex biological mechanisms. The authors adopted rigorous physical and advanced data‐driven models to simulate the dynamics of this system subject to incomplete and scarce datasets (imitating industrial situations). Performance of these modelling approaches was compared and previously proposed algae‐bacteria interactions were successfully verified. Finally, a heuristic model selection procedure was provided for future complex bioprocess modelling.