2023 AIChE Annual Meeting

Magma: A Robust, User-Friendly Kinetic Modeling Platform for Simulating and Optimizing Algal Bioprocesses

Micro-algal processes have broad impacts on wastewater treatment, environmental remediation, and bio-production. For monitoring natural algal growths or optimizing photobioreactor (PBR) operations, it is important to predict and control algal cultivation parameters including light, bioreactor size and configurations, temperature, CO2 and nutrient conditions. Kinetic models of micro-algae systems are widely used for elucidating photoautotrophic growth phenomena and performance. However, programming numerical methods to evaluate systems of ordinary differential equations (ODE) representing micro-algae systems (e.g., Monod Models) can be a time-consuming and error-prone process. The Micro-Algae Growth Modeling Application (MAGMA) is a user-friendly, simple, yet powerful tool designed to streamline the development of kinetic growth models based on empirical data for predicting growth phenomena. MAGMA offers the following functionality: 1. Specification of system environmental conditions, biological and chemical species in the system, and the governing differential equations that describe the rate of change of each species in the system, 2. Specification of mass transfer flows in and out of the system, 3. Parameter regression to fit models to empirical data, 4. Optimization for PBR cell and chemical productivity, 5. Kinetic visualization tools, 6. Cloud database integration to enable users to access model templates and models created by other users, and 7. Integration of kinetic model with PBR controls (e.g., model predictive control).

MAGMA is free for academic use and is designed so that a user’s lack of prior programming experience will not be a hinderance to their usage of the tool. For validating MAGMA functionality, a case study on micro-algae growth and toxin production have been performed. Based on experimental data, MAGMA was used to evaluate harmful algal growth and toxin production by cyanobacterium Microcystis aeruginosa. Specifically, the model evaluated the toxin reduction effectiveness of nutrient reduction, herbicide application, and competitive co-culture with a fast-growing, non-toxic species of cyanobacteria (Synechoccocus elongatus UTEX 2973). The models generated with MAGMA based on the data from this study included mono-culture and co-culture batch growth kinetics, batch nutrient consumption kinetics, batch microcystin toxin production kinetics, batch herbicide consumption kinetics, final cell concentration in mono-culture and co-culture across varying initial nutrient concentration conditions, and final microcystin toxin concentration across varying initial nutrient concentration conditions. MAGMA is a robust kinetic modeling framework designed to enhance knowledge of micro-algae growth phenomena by connecting microbial growth theory to experimental observations.

Keywords: Kinetic modeling, micro-algae, photo-bioreactor (PBR), MAGMA, ordinary differential equations, mass transfer, regression, optimization, model visualization, cloud database