A Comprehensive Benchmark Platform for Process Control Research of Outdoor Microalgae Raceway Reactors

TL;DR

The paper tackles the lack of open, high-fidelity benchmarks for controlling outdoor microalgae raceway ponds with four coupled regulation tasks: pH, DO, harvest-dilution, and temperature under realistic diurnal disturbances. It introduces a first-principles, experimentally calibrated dynamic model and a Matlab-based closed-loop benchmark with clearly defined interfaces for user-supplied controllers, a unified performance index, and comprehensive KPIs. Four baseline controllers—On/Off, PI, turbidostat-like, and EMPC—are evaluated to reveal how feedback and optimization affect stability, productivity, and sustainability. The benchmark provides a practical, reproducible platform to bridge control theory and open-pond algal bioprocess engineering, enabling multivariable controller development and fair comparisons across approaches.

Abstract

This paper presents a benchmarking framework to evaluate process control strategies in outdoor microalgae raceway reactors, integrating four key control regulation tasks: pH, dissolved oxygen (DO), culture volume through coordinated harvest-dilution actions, and temperature via a sump-mounted spiral heat exchanger. The benchmark is built upon a high-fidelity, experimentally calibrated dynamic model that captures the strongly coupled thermal, physicochemical, and biological processes governing industrial-scale open raceway ponds. A closed-loop simulation environment is provided, featuring realistic actuator constraints, gas transport delays, stiff integration, and a fully specified scenario based on multi-day outdoor disturbances (irradiance, temperature, wind, and humidity). Four user-replaceable controllers define the manipulation of CO2 injection, air bubbling, harvest/dilution sequencing, and heat-exchanger operation. The platform computes a unified global performance index, in addition to individual metrics for each control problem, combining tracking error, gas and energy usage, and biomass productivity, enabling consistent and quantitative comparison of alternative control strategies. Baseline regulatory architectures (On/Off, PI/PID, and Economic Model Predictive Control (EMPC)) are included to illustrate the benchmark use for classical and advanced control methods. By providing an openly specified, reproducible, and computationally tractable benchmark with well-defined function interfaces, this work aims to bridge control methodology and outdoor algal bioprocess engineering, and to support the development of multivariable control strategies for disturbance-rich environmental systems.

Figures (11)

• Figure 1: Microalgae raceway reactor at the IFAPA facilities (Almería, Spain).
• Figure 2: Architecture of the raceway benchmark model. The model is decomposed into three interacting submodels: (i) a thermal submodel (energy balance), (ii) a biological submodel (microalgae growth) and (iii) an engineering submodel (mass transfer, gas transfer, and carbonate system). The three submodels share a common state vector and are driven by meteorological disturbances and actuator signals.
• Figure 3: Model validation over a six-day period. Comparison between simulation (solid lines) and measurements (dashed lines) for pH, dissolved oxygen (DO), CO$_2$ injection, air flow, reactor temperature, microalgae biomass concentration, solar radiation, external temperature, relative humidity, and wind speed (REVISAR LEYENDAS).
• Figure 4: Control scheme used in Player 1 configuration
• Figure 5: Player 1 simulated trajectories. From top to bottom: pH, DO, biomass concentration, water depth and water temperature trajectories. In blue, the variable value. In dashed black, the setpoints.