Abdellatif Soussi

As global food security becomes increasingly critical due to population growth and climate change, greenhouses play a vital role in producing high-quality crops in controlled environments. This paper aims to develop and implement an advanced and comprehensive energy management platform based on the Quantum Particle Swarm Optimization (QPSO) algorithm that is capable of performing specialized monitoring and control services for smart greenhouses. The control platform comprises communication-assisted decision-making and control modules developed and tuned for heterogeneous power distribution systems that accommodate renewable energy generators, HVAC, energy storage device, pumps, water reservoir CO2 injector, dehumidifier, smart meters, and sensors. The main objective is to formulate and implement a dynamic nonlinear QPSO (NQPSO) framework embedded in a predictive model to optimally control the operation of the greenhouse and manage the power flow, ensuring highly efficient use of energy. The framework aims to ensure optimal conditions for crop growth by accurately regulating indoor microclimate variables while minimizing energy exchange with the grid during periods of low renewable energy generation, thus ensuring efficient operation. The predictive NQPSO framework is compared with classical optimization Particle Swarm Optimization (PSO) and Pyomo with Ipopt as a solver through extensive numerical simulations, demonstrating its superior performance and effectiveness.

The Dangerous Goods Transportation (DGT) presents significant challenges, requiring a strong and systematic risk assessment framework to ensure the safety and efficiency of the supply chain. This study addresses a critical gap by integrating a deterministic and holistic approach to risk assessment and management. Utilizing Geographic Information Systems (GIS), meteorological data, and material-specific information, the research develops a data-driven approach to identify analyze, evaluate, and mitigate risks associated with DGT. The main objectives include monitoring dangerous goods flows to identify critical risk areas, optimizing emergency response using a shared model, and providing targeted training for stakeholders involved in DGT. The study leverages Information and Communication Technologies (ICT) to systematically collect, interpret, and evaluate data, producing detailed risk scenario maps. These maps are instrumental in identifying vulnerable areas, predicting potential accidents, and assessing the effectiveness of risk management strategies. This work introduces an innovative GIS-based risk assessment model that combines static and dynamic data to address various aspects of DGT, including hazard identification, accident prevention, and real-time decision support. The results contribute to enhancing safety protocols and provide actionable insights for policymakers and practitioners aiming to improve the resilience of technological systems for road transport networks handling dangerous goods.

This article introduces a digital twin framework for greenhouse climate management, utilizing model predictive control (MPC) to enhance smart farming practices and tackle the challenge of maintaining optimal conditions in diverse weather scenarios. The proposed MPC integrates dynamic system modeling, predictive control, and constraint management to regulate critical variables such as ambient temperature, solar radiation, and airflow. To validate the control framework, historical meteorological data from the city of Savona are used for four seasonal scenarios: winter, spring, summer, and autumn. The main contributions include designing and tuning the MPC, applying soft constraints via equal concern for relaxation for feasibility, and implementing scaling factors to ensure numerical stability. Simulation results demonstrate the controller’s ability to maintain desired climatic conditions while meeting physical and operational constraints. This work highlights the potential of combining digital twin technology with advanced control systems to support sustainable and efficient smart farming.

The global agricultural sector faces increasing challenges in adopting sustainable practices and reducing its environmental footprint. Smart greenhouse agriculture has emerged as a key solution, enabling efficient year-round crop production while minimizing dependence on traditional field farming. However, achieving near-zero energy consumption in greenhouses remains a major challenge due to the high operational energy demands. This review examines the current state of energy consumption in greenhouses, critically analyzes existing technological solutions, and identifies key challenges, such as high energy consumption for heating, cooling, and lighting. The study highlights opportunities for integrating renewable energy sources, optimizing energy-saving systems, and using advanced control technologies such as artificial intelligence (AI) and the Internet of Things (IoT) to monitor microclimatic conditions. Results show that integrating these solutions can significantly reduce energy consumption while maintaining optimal growing environments. The main findings include prioritizing the adoption of hybrid renewable energy systems, improving greenhouse design and material selection, and enhancing real-time monitoring systems with smart technologies. Future research should focus on cost-effective innovations, interdisciplinary approaches, and the scalability of energy-efficient designs. This review provides actionable information for researchers, policymakers, and practitioners to advance the transition to sustainable, near-zero energy greenhouse systems.

Today’s increasingly complex energy systems require innovative approaches to integrate and optimize different energy sources and technologies. In this paper, we explore the system of systems (SoS) approach, which provides a comprehensive framework for improving energy systems’ interoperability, efficiency, and resilience. By examining recent advances in various sectors, including photovoltaic systems, electric vehicles, energy storage, renewable energy, smart cities, and rural communities, this study highlights the essential role of SoSs in addressing the challenges of the energy transition. The principal areas of interest include the integration of advanced control algorithms and machine learning techniques and the development of robust communication networks to manage interactions between interconnected subsystems. This study also identifies significant challenges associated with large-scale SoS implementation, such as real-time data processing, decision-making complexity, and the need for harmonized regulatory frameworks. This study outlines future directions for improving the intelligence and autonomy of energy subsystems, which are essential for achieving a sustainable, resilient, and adaptive energy infrastructure.

Precision agriculture, driven by the convergence of smart sensors and advanced technologies, has emerged as a transformative force in modern farming practices. The present review synthesizes insights from a multitude of research papers, exploring the dynamic landscape of precision agriculture. The main focus is on the integration of smart sensors, coupled with technologies such as the Internet of Things (IoT), big data analytics, and Artificial Intelligence (AI). This analysis is set in the context of optimizing crop management, using resources wisely, and promoting sustainability in the agricultural sector. This review aims to provide an in-depth understanding of emerging trends and key developments in the field of precision agriculture. By highlighting the benefits of integrating smart sensors and innovative technologies, it aspires to enlighten farming practitioners, researchers, and policymakers on best practices, current challenges, and prospects. It aims to foster a transition towards more sustainable, efficient, and intelligent farming practices while encouraging the continued adoption and adaptation of new technologies.

The global agricultural sector is increasingly pressured to adopt sustainable practices and reduce its environmental impact. In this context, greenhouses play a crucial role in enabling year-round crop production, ensuring food security, and minimizing reliance on traditional open-field farming. However, the energy consumption associated with greenhouse operations poses a significant challenge to achieving sustainability goals. As a result, there is a growing emphasis on transitioning greenhouses towards near-zero energy consumption. Near-zero energy consumption in greenhouses refers to the ambitious objective of minimizing energy usage to the greatest extent possible while maintaining optimal growing conditions for crops. This goal encompasses reducing energy consumption for heating, cooling, lighting, and other operational needs, as well as exploring renewable energy sources to power greenhouse operations. This review article offers a comprehensive overview of greenhouse energy consumption, with the main goal of analyzing the present situation, identifying key challenges, exploring potential opportunities, and proposing future perspectives for decreasing energy usage in greenhouse environments. As the focus on sustainable agricultural practices grows, the need to reduce energy consumption in greenhouses becomes increasingly important. The review critically examines current technological models and strategies applied in smart greenhouse applications, as well as the monitoring of microclimatic conditions inside the greenhouse, encompassing factors such as temperature, humidity, CO2 levels, soil quality, and crop cultivation. Moreover, it aims to present existing literature that investigates the advancement of greenhouses toward achieving significant reductions in energy consumption.