A novel data-driven model for prediction and adaptive control of pH in raceway reactor for microalgae cultivation

This work proposes a new data-driven model to estimate and predict pH dynamics in freshwater raceway photobioreactors. The resulting model is based purely on data measured from the reactor and divides the pH dynamics into two different behaviors. One behavior is described by the variation of pH due to the photosynthesis phenomena made by microalgae; and the other comes from the effect of CO2 injections into the medium for control purposes. Moreover, it was observed that the model parameters vary throughout the day depending on the weather conditions and reactor status. Thus, a decision tree algorithm is also developed to capture the parameter variation based on measured variables of the system, such as solar radiation, medium temperature, and medium level. The proposed model has been validated for a data set of more than 100 days during 10 months in a semi-industrial raceway reactor, covering a wide range of weather and system scenarios. Additionally, the proposed model was used to design an adaptive control algorithm which was also experimentally tested and compared with a classical fixed parameter control approach.

An artificial intelligence approach for identification of microalgae cultures

In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The characterization of microalgae cultures is essential to guarantee the quality of the biomass, and the objective of this work is to achieve a simple and fast method to address this issue. Data acquisition was performed using FlowCam, a device capable of capturing images of the cells detected in a culture sample, which are used as inputs by the model. The model can distinguish between 6 different genera of microalgae, having been trained with several species of each genus. It was further complemented with a classification threshold to discard unwanted objects while improving the overall accuracy of the model. The model achieved an accuracy of up to 97.27% when classifying a culture. The results demonstrate the effectiveness of the Deep Learning models for the characterization of microalgae cultures, it being a useful tool for the monitoring of microalgae cultures in large-scale production facilities while providing accurate characterization over a wide range of genera.

Modelado y control de la producción de microalgas en fotobiorreactores industriales

<p>Este artículo presenta una visión general sobre el proceso de producción de microalgas desde un punto de vista de modelado y control de procesos. En primer lugar se exponen las ventajas y el potencial de este tipo de microorganismos, así como los distintos tipos de reactores que se suelen utilizar para su producción. Posteriormente, se analiza el comportamiento dinámico de este tipo de procesos, el cual es muy complejo y cambiante debido a variaciones en las condiciones ambientales tanto diarias como anuales, y se presentan los distintos balances que permiten describir la evolución de las principales variables del sistema. Se exponen distintos tipos de modelos a nivel biológico y a nivel estructural que han sido validados a escala industrial. Tras analizar su comportamiento dinámico, se motivan los distintos problemas de control existentes en este tipo de sistemas y se resume una amplia batería de estrategias de control que han sido evaluadas con éxito en fotobiorreactores industriales. Finalmente, se concluye el trabajo con un balance de los aspectos más importantes expuestos a lo largo del mismo.</p>

Indirect regulation of temperature in raceway reactors by optimal management of culture depth

Temperature and irradiance are the two most relevant factors determining the performance of microalgae cultures in open raceway reactors. Moreover, inadequate temperature strongly reduces the biomass productivity in these systems even if enough sunlight is available. Controlling the temperature in large open raceway reactors is considered unaffordable because of the large amount of energy required. This study presents an indirect method for temperature regulation in microalgae raceway reactors by optimizing the culture depth. First, the effect of the culture depth on the raceway temperature is analyzed for different seasons of the year. Afterward, a simulation study is presented where the proposed control approach is compared to the normal operation mode with constant volume in the reactor. This study is also extended to industrial scale. Relevant improvements on the temperature factor and biomass production are presented. The developed knowledge allows the improvement of the performance in open raceway reactors up to 12% without involving additional energy and costs, being a suitable solution for large industrial reactors that until now have no options for controlling the temperature.

Diurnal and nocturnal pH control in microalgae raceway reactors by combining classical and event-based control approaches

The pH control in raceway reactors is crucial for an optimal performance of the system. Classical pH control is exclusively performed during the daytime period for cost saving reasons. This paper demonstrates that pH can be controlled 24 hours a day by using both a continuous-based and an event-based control approach, being able to improve the system's performance and reducing costs at the same time. Thus, experimental tests on a raceway reactor for several days are presented to show a comparison between traditional control algorithms during the daytime period versus an event-based control approach operating during both daytime and night-time periods. As a result, the combination of classical PI control for the daytime period and the event-based control for the night-time period is presented as a promising pH control architecture in raceway reactors.

P0664CKDSENS, A NOVEL HEALTHCARE PLATFORM FOR CHRONIC KIDNEY DISEASE

Background and Aims

The 10% of the global adult population, more than 730 million people, suffers from some degree of chronic kidney disease (CKD). From that population, a 30% is suffering from an advanced stage of the disease. Approximately 38 million of the European population is affected by an advanced stage renal disease with 1 million of new cases every year. These patients require close monitoring, which increase the burden of the disease. New tools and methods are required to improve the patients’ well-being, the perception and management of their disease, decrease the impact of the complications, as well as to delay of the progression of the disease.

Method

Seven partners have joined in the CKDSens consortium and are contributing to the project with different expertise and technical resources (medical, technological, educational, business and management): CreatSens Health SL, Hospital Clínic de Barcelona – Fundació Privada Clínic per a la Recerca Biomèdica, Genesis Biomed, Eurecat, Universitat de Barcelona, Universidad Politécnica de Madrid, and Madopa. This consortium is a novel and unique technological platform, combining in vitro diagnostic devices and software components, that connects patients with CKD and healthcare professionals to improve the well-being of people and the efficiency of the healthcare system by monitoring relevant biomarkers out of the hospital in a simple and cost- effective connected paper-based sensors. The proposal aims to increase the quality of life of CKD patients through early diagnostics, real time monitoring and personalization of therapies.

Results

The CKDSens consortium is developing a novel technological platform for real-time and low-cost diagnostics for two key biomarkers in renal diseases (creatinine and potassium), which include a new point-of-care device that allows monitoring by remote control these biomarkers. The technology has been patented for both biomarkers, and it has been transferred to CreatSens, a spinoff from Universitat Rovira i Virgili. The product integrates paper-based sensors in a drop of blood analysis, wireless connectivity and a digital platform as data analytics. The aim of the consortium is to validate the use of a new medical device as a product that can fit and truly generate value and business. Through that perspective, the optimization, manufacturing and validation of the tool and the business case will be performed as a first step, while usability, end user interaction and regulatory pathway will be developed in the second step. CKDSens will afford a novel approach to embed healthcare in daily life and routine. This new strategy implies production and commercialization for prevention and diagnose where markets are well known and validated. The aim is to mimic a well-known business model such as glucometer, translating this one into renal diseases. CKDSens has risen funding for this purpose through a grant of the European Institute of Innovation and Technology (EIT Health, ID Project 19579), and it is working to have validated this device at the end of the year. This requires also a clinical validation, which will be performed in a clinical trial in the second half of the year.

Conclusion

CKDSens consortium has been created as a novel technological platform to develop and validate a new medical device for a remote control of patients with CKD, in order to increase the quality of life of CKD patients through early diagnostics, real time monitoring and personalization of therapies. It is expected to have this device designed and validated by the end of the year 2020 and ready for further commercialization.

Multiobjective control architecture to estimate optimal set points for user comfort and energy saving in buildings

Temperature control in buildings is usually driven by energy conservation although the occupants' comfort is also important considering its impact on productivity and health. However, energy efficiency and comfort are opposing objectives and therefore this type of problem can be resolved by means of a multiobjective optimization approach. The simulations we carried out indicate that set points optimization has the potential to reduce energy consumption in the order of 10% while also providing a comfortable work environment for the occupants.

Application of SSOD-PI and PI-SSOD event-based controllers to greenhouse climatic control

In this work, an application of the Symmetric Send-On-Delta (SSOD) event-based controllers to the inside air temperature control of the greenhouse production process is presented. The control technique analysis is split into two stages. The first stage is devoted to determine the proper controller parameters and to check the influence of the Send-On-Delta (SOD) threshold value through simulation study. At the second stage, experimental tests on the real greenhouse facilities are performed. The obtained results show that the analyzed control techniques handle the control task with desired accuracy and performance. In particular, the proposed control system saves costs related with energy consumption and wear minimization, by achieving a satisfactory performance at the same time.

Effective utilization of flue gases in raceway reactor with event-based pH control for microalgae culture

This work addresses effective utilization of flue gases through the proper pH control in raceway reactors. The pH control problem has been addressed with an event-based control approach using a Generalized Predictive Controller (GPC) with actuator deadband. Applying this control strategy it is possible to reduce the control effort, and at the same time saving control resources. In the pH process case, the event-based controller with actuator deadband can be tuned to supply only necessary amount of CO2 to keep the pH close to its optimal value. On the other hand, the evaluated control algorithm significantly improves the pH control accuracy, what has a direct influence on biomass production. In order to test the performance of the event-based GPC controller, several experiments have been performed on a real raceway reactor. Additionally, several control performance indexes have been used to compare the analyzed technique with commonly used on/off controller.

Dynamic model of microalgal production in tubular photobioreactors

A dynamic model for microalgal culture is presented. The model takes into account the fluid-dynamic and mass transfer, in addition to biological phenomena, it being based on fundamental principles. The model has been calibrated and validated using data from a pilot-scale tubular photobioreactor but it can be extended to other designs. It can be used to determine, from experimental measurements, the values of characteristic parameters. The model also allows a simulation of the system's dynamic behaviour in response to solar radiation, making it a useful tool for design and operation optimization of photobioreactors. Moreover, the model permits the identification of local pH gradients, dissolved oxygen and dissolved carbon dioxide; that can damage microalgae growth. In addition, the developed model can map the different characteristic time scales of phenomena inside microalgae cultures within tubular photobioreactors, meaning it is a valuable tool in the development of advanced control strategies for microalgae cultures.

Minimization of carbon losses in pilot-scale outdoor photobioreactors by model-based predictive control

The optimization of carbon use in pilot-scale outdoor tubular photobioreactors is investigated in this study. The behavior of a 0.20-m(3) tubular photobioreactor was studied, with and without algae, by steady-state and pulse dynamic-response analysis experiments. A model of the system was obtained and implemented in a programmable control unit and was used to control the reactor under normal production conditions. Results showed that, using and on-off control, the mean daily CO(2) flow in the reactor was 0.86 g min(-1), 19.7% of this being lost. By using a predictive control algorithm the mean daily CO(2) flow was reduced to 0.74 g min(-1), with losses being reduced to 15.6%. In this case, pH tracking was not adequate, especially at the beginning and end of the daylight period, because the variation in solar irradiance was not considered. Taking solar irradiance into account resulted in better performance, with mean daily CO(2) flow reduced to 0.70 g min(-1), and carbon losses reduced to 5.5%. pH tracking was improved and valve actuation was reduced. Improvement of pH control reduced pH gradients in the culture, which increased the photosynthesis rate and biomass productivity of the system. Biomass productivity increased from 1.28 to 1.48 g L(-1) day-(1) when on-off control was replaced by model-based predictive control plus solar irradiance effect mode. Implementation of this methodology in outdoor photobioreactors can increase productivity by 15% and reduce the cost of producing biomass by >6%. Clearly, application of effective control techniques, such as model-based predictive control (MPC), must be considered when developing these processes.