Publications

We’ve demonstrated a novel solar-powered solution for urban wastewater reclamation, applying solar disinfection (SODIS) enhanced with solar concentration to make crop irrigation safer and more sustainable. At the heart of the project is a pioneering demonstration plant in Almería, Spain, built around a 36 m² Fresnel solar collector and designed for continuous-flow operation. Unlike conventional systems, this setup integrates renewable energy directly into wastewater treatment, significantly cutting operational costs while reducing environmental impacts. To further boost performance, solar/H₂O₂ enhancement was explored, leading to even greater microbial inactivation. This breakthrough shows how solar-based technologies can advance wastewater reuse in agriculture, supporting resource efficiency, food security, and climate resilience.

Authors: Daniel Rodríguez-García, José Luis García Sánchez, José Luis Guzmán, Zouhayr Arbib, José Luis Casas López, José Anttonio Sánchez Pérez

In this newly published study, we present an innovative Virtual Lab (VL) built around a Solar Collector Field (SCF), offering students and engineers a hands-on environment to master key control strategies—including PID, predictive, and nonlinear control—under realistic solar energy conditions, which can be vital for microalgea process usng renewable energy. By enhancing digital learning and system modeling skills, this educational tool bridges the gap between theory and application in sustainable process engineering. The SCF Virtual Lab is freely accessible online on any device—bringing advanced control learning to your fingertips.

Authors: Igor M. L. Pataro; Juan D. Gil; José Luis Guzmán; Manuel Berenguel

We’ve developed an adaptive autotuning controller to keep pH levels in microalgae raceway reactors precisely regulated—a key factor for maximizing biomass productivity. Our approach builds on relay-based autotuning, but with a twist: instead of acting on the control signal, the relay is applied directly to the setpoint, allowing seamless integration into existing control loops without seasonal recalibration. The system also incorporates a weather-classification algorithm to adapt to clear or cloudy conditions, ensuring stable performance under fluctuating sunlight. This innovation paves the way for scalable, automated pH regulation in microalgae cultivation, supporting more efficient, resilient, and sustainable bioresource production and enhaced wastewater treatment.

Authors: Malena Caparroz, Kristian Soltesz, Tore Hägglund, José Luis Guzmán, Manuel Berenguel

Discover how advanced control engineering is harnessing the power of the sun at the Plataforma Solar de Almería. Using a robust Quantitative Feedback Theory (QFT) approach, researchers have developed a high-performance temperature control system for solar furnace applications—successfully managing uncertainty, solar fluctuations, and system safety. This strategy, combining feedforward control, PI regulation, and anti-windup protection, has proven effective under real conditions. Excitingly, the same control methodology can be adapted to optimize photobioreactor systems, improving the efficiency of wastewater treatment with renewable energy solutions.

Authors: Ángeles Hoyo; José Carlos Moreno; José Luis Guzmán; Juan D. Gil; Manuel Berenguel

We’ve developed a smart, learning-based controller to boost the performance of solar collector fields (SCFs) used in thermal energy systems. This new approach—called LPNMPC—adapts in real-time to system changes like temperature swings, parameter shifts, and cloudy weather. Tested on a real SCF model from the CIESOL research center (Almería, Spain), it reduced errors by up to 27% compared to current predictive methods. By combining learning algorithms with robust control, our system ensures precise temperature regulation and stable operation of solar-powered photobioreactors, even under fluctuating environmental conditions. This approaches can boost renewable energy and wastewater treatment momentum, enabling efficient, resilient, and sustainable bioresource recovery systems.

Authors: Igor M. L. Pataro, Juan D. Gil, José D. Álvarez, José L. Guzmán, João M. Lemos, Manuel Berenguel

In this work we introduce a next-generation IoT platform tailored for the agro-industrial sector, tackling key challenges in digital transformation such as data heterogeneity, poor interoperability, and limited scalability. Developed using open-source technologies and the FIWARE framework with the OMA NGSI standard, this platform enables seamless integration of diverse devices and systems—paving the way for smarter, more connected industrial operations. With a scalable architecture and embedded industrial models (e.g., climate, production), it supports complex real-world scenarios and has been validated across three different case studies. Extensive testing in a cloud environment confirms its ability to manage high loads while optimizing resource use. A solid step forward in bridging the gap between digital innovation and industrial application!

Authors: Manuel Muñoz Rodriguez, Manuel Torres Gil, Juan Diego Gil, José Luis Guzmán

Recently, we have dived into two leading digital models—ABACO-2 and ALBA—used to simulate and optimize microalgae-based wastewater treatment. These nature-based systems not only help clean water but also generate valuable biomass. By comparing these models across different environmental conditions and locations (Almería, Milan, and Narbonne), we aim to understand their strengths, limitations, and the practical challenges of applying them to new datasets. This work highlights the urgent need for standardized modelling approaches that can adapt to real-world conditions. Key issues like data management, parameter calibration, and monitoring requirements are still hurdles to wider adoption and industrial scale-up. As we move toward a more sustainable and circular bioeconomy, robust and adaptable digital tools like ABACO-2 and ALBA will play a crucial role in transforming wastewater into a resource. This is just the beginning of smarter, greener water solutions!

Authors: Rebecca Nordio, Francesca Casagli, Enrique Rodriguez, José Luis Guzmán, Olivier Bernard, Gabriel Acién

Our latest publication presents the first-ever full-scale membrane distillation unit powered by biomass to treat reverse osmosis brine—tailored specifically for greenhouse agriculture. Conducted at IFAPA’s AgroConnect facilities in Almería, this innovative system pushes the boundaries of sustainable water management. The achieved results demonstrate that full-scale vacuum-assisted air-gap membrane distillation can take part in a future optimized sustainable water network to supply water for intensive agricultural activity in the south-east of Spain, although a sufficient cooling supply is mandatory to avoid operational issues. A step closer to a circular and climate-resilient food system!

Authors: Juan Antonio Andrés-Mañas, Juan Diego Gil, Jorge Antonio Sanchez-Molina, Manuel Berenguel, Guillermo Zaragoza

Discover how cutting-edge technology is revolutionizing sustainable water desalination! Our latest research explores the power of data-driven modeling in optimizing solar-powered membrane distillation (MD) systems. By integrating innovative mirrors into solar collector fields, we enhance thermal energy capture, pushing the boundaries of traditional desalination efficiency. With advanced artificial intelligence techniques, our study identifies the most accurate model for predicting system performance, achieving remarkable precision (R² = 0.9741). This breakthrough paves the way for smarter, more efficient desalination solutions—tackling water scarcity with renewable energy. Dive into the future of sustainable water production with us!

Authors: Alejandro Bueso Sánchez, Juan Diego Gil Vergel, Guillermo Zaragoza

Our latest study introduces a hybrid Model Reference Adaptive Control (MRAC) strategy to enhance pH regulation in open raceway reactors for microalgae production, addressing the challenges posed by nonlinear and time-varying system dynamics. Conducted at the IFAPA center-University of Almería, the research demonstrates that the MRAC controller significantly outperforms traditional control methods by dynamically adapting to varying conditions, ensuring precise and stable pH regulation. Through real-world experiments spanning twelve days in different months, the proposed approach proved its robustness and industrial viability, contributing to the advancement of adaptive control strategies for sustainable and cost-effective microalgae production.

Authors: Malena Caparroz, José Luis Guzmán, Juan Diego Gil, Manuel Berenguel, Francisco Gabriel Acién

In our study, we introduce an innovative, low-cost artificial intelligence approach for detecting contamination in microalgae production systems. By developing a neural network that classifies microalgae genera using spectral data (300–750 nm range) and analyzing the softmax layer output, we achieve highly accurate contamination detection. Trained on pure samples of four microalgae genera—Spirulina, Chlorella, Synechococcus, and Scenedesmus—the model demonstrated a macro F1 score of 98.64% during validation. Further testing in different photobioreactors confirmed its reliability for real-world applications, offering a practical solution for continuous monitoring without the need for costly equipment or specialized personnel. This approach enhances reactor maintenance by enabling early contamination detection, supporting more efficient and sustainable microalgae production.

Authors: José Gonzalez-Hernández, Martina Ciardi, José Luis Guzmán, José Carlos Moreno, Francisco Gabriel Acién