A computer-based tool to simulate raceway photobioreactors for design, operation and control purposes

Highlights

• A simulator to evaluate industrial photobioreactors.

• Photosynthetic, design and operational models implemented into a simulator tool.

• Different strains and photobioreactor designs can be simulated in the different year seasons.

• Different control approaches can be evaluated for a tradeoff between performance and costs.

Abstract

Microalgae cultivation in photobioreactors is a complex process where environmental, biological and/or design variables affect the system. Due to this great complexity, it is important to have tools to analyze the system in a clear and simple way. This work presents a new software tool for the simulation of microalgae production in raceway photobioreactors. The tool allows to simulate the process on different seasons of the year, using different microalgal strains and considering different reactor designs. Moreover, open-loop and closed-loop control strategies can be analyzed, such as On/Off or PI controllers for pH and dissolved oxygen variables, in order to increase the system productivity. Thus, estimations for productivity and gas consumption are also given to compare several simulated scenarios. Real data for the weather variables are taken from a semi-industrial raceway to be used as inputs to the simulation tool.

Introduction

The importance of environmental sustainability and the need to make use of renewable energy promotes the discovery of new systems, such as the cultivation of microalgae in industrial photobioreactors. These biological processes are well known nowadays due to the great potential of the resulting biomass obtained for energy purposes, as well as other derived products such as cosmetics, animal feed, or for the wastewater treatment (Jebali et al., 2015).

There are mainly two types of reactors to produce microalgae: closed reactors (such as tubular reactors) and open reactors (such as raceway reactors). The reactors most widely used are the raceway ones, mainly due to their low initial investment cost compared to the closed ones, which require more complex structures and equipment. Raceway photobioreactors have been studied since 1950 in order to provide a solution for the cultivation of microalgae at industrial scale. Currently, due to their scalability and feasibility, they are considered the most suitable technology for large-scale cultivation of microalgae. Other relevant advantages of raceway reactors are the simplicity of operation and their low maintenance costs (Weissman and Goebel, 1987). Numerous studies have focused on the optimal selection of the design and configuration of these photobioreactors to ensure that the microalgae grow in the best conditions. Nowadays, there are numerous photobioreactor designs that look for optimal growth conditions (Chen, Yeh, Aisyah, Lee, Chang, 2011, Pawlowski et al., 2014), and which vary slightly from the original design proposed by Oswald and Golueke (1960).

On the other hand, these biological systems have very complex dynamics, being difficult to be modelled and controlled (Ippoliti et al., 2016). In the past few years, different models have been developed, specially based on first principles as that presented in Fernndez et al., 2014, Solimeno et al. (2015). Moreover, biological models have also been proposed (Lee, Jalalizadeh, Zhang, 2015, Guterman, Vonshak, Ben-Yaakov, 1990, James, Boriah, 2010, Jupsin, Praet, Vasel, 2003). In Bernard and Rmond (2012), a new model is defined that captures the effect of temperature and light on microalgae growth, predicting the productivity of microalgae in open reactors. In Zambrano et al. (2016), a new biological model was presented for the combination with wastewater treatment. In Sánchez-Zurano et al., 2020 a microalgae-bacteria model based on photosynthesis and respiration rates for wastewater processes using microalgae is developed and validated. Also, an improvement of the photosynthetic efficiency in a 100 m${}^2$ raceway reactor is carried out in Barcel-Villalobos et al., 2019 by improving the light regime to which the cells are exposed. Moreover, in Béchet et al. (2011), Rodríguez-Miranda et al., 2021 a new temperature model has been developed to determine the temperature of the microalgae culture as a function of reactor design and environmental conditions.

In the microalgae production process, the most important variables that affect their growth are solar radiation, temperature, pH and dissolved oxygen (Costache et al., 2013). For raceway reactors, the solar radiation incidence requirements and temperature operating conditions are generally determined by the system architecture itself (Rodríguez-Miranda et al., 2021). However, the pH and the dissolved oxygen have a highly dependent dynamics on the photosynthesis process and it is necessary to keep them close to the desired operating points, regardless of the system architecture (Pawlowski et al., 2015).To keep the variables between desired values automatically and efficiently, it is necessary to implement control engineering approaches (Åström and Hägglund, 2006, Ellis, 2012, Franklin, Powell, Emani-Naeni, 2015). This is based on feedback, which consists of redirecting a certain proportion of the output signal of a system towards the input (Åström and Murray, 2014). An automatic feedback controller compares the value of the system output with the desired value (setpoint), determines the tracking error and provides a control action that will attempt to reduce the error to zero, or to a very small value.

During last years, different control strategies have been developed to control pH and/or dissolved oxygen in photobioreactors. Of these two variables, pH can be considered the most important variable to control, since it has a direct influence on the photosynthesis and, therefore, on the on the microalgae growth (Costache, Acién, Morales, Fernndez-Sevilla, Stamatin, Molina, 2013, Molina, Fernndez, Pérez, Camacho, 1996, Pawlowski et al., 2019). The most widely used pH control strategy is the On/Off control (Wang, Wen, Xu, Ding, Geng, Li, 2018, Mehar, Shekh, M. U., Sarada, Chauhan, Mudliar, 2019). A nonlinear continuous-time Model Predictive Control (MPC) method is presented in Oblak and Ackrjanc (2010), Arumugasamy and Ahmad (2012). An event-based Generalized Predictive Controller (GPC) with a disturbance compensation approach is used in Pawlowski et al., 2014 to manage CO${}_2$ effectively. Afterwards, this GPC scheme was implemented combined with a selective control strategy between pH and dissolved oxygen in a microalgae raceway photobioreactor, presented in Pawlowski et al., 2015. In that control approach, pH control is prioritized over dissolved oxygen since it has a critical influence on the process, as previously mentioned. With this control scheme, it is possible to improve the growth conditions of the microalgae, increasing the total production. On the other hand, in Rodríguez-Miranda et al., 2020 an implementation and comparison of both PI and an event-based pH controllers on a microalgae raceway photobioreactor during the day and night is performed. The study demonstrated the advantages of the implementation of this control architecture compared to a classic On/Off control running only during the daytime period, reducing the control errors and improving the operating conditions of the reactor.

Thus, the complexity of these processes clearly arises from a design, operation, modelling and control points of view (Guzmán et al., 2021). In this sense, there is a need to develop tools that allow the user to easily simulate the behaviour of the system and to modify the main parameters from biological, design and operation perspectives. Therefore, in this work, a graphical tool for the simulation of the raceway reactor is presented. This tool includes the nonlinear models developed in Sánchez-Zurano et al., 2020, Fernández et al. (2016) and Rodríguez-Miranda et al., 2021, and the control algorithms presented in Pawlowski et al., 2015 and Rodríguez-Miranda et al., 2020. It has been developed using M-code and the App Designer from Matlab (MathWorks, 2021). It allows to access and to modify the most important variables of the process, to simulate the system and to observe the results in a straightforward and graphical way. Notice that the proposed tool also permits to modify the reactor structure and its design. Moreover, different strains can be studied by including their biological parameters. Real data related to the weather variables from a meteorological station of an industrial raceway reactor are used as inputs to the tool covering different seasons of the year. Furthermore, several control approaches can be analyzed for pH and dissolved oxygen variables in order to study the impact on the biomass productivity.

The paper is organized as follows. A description on the modelling and control background behind the tool is presented in Section 2, together with brief information about the raceway reactor used as example. A summary of the tool functionality is presented in Section 3. Illustrative examples are presented in Section 4. Finally, the paper ends with conclusions.