Jos� Maria Ponce-Ortega – författare

Strategic Planning for the Sustainable Production of Biofuels presents several optimization models for the design and planning of sustainable biorefinery supply chains, including issues surrounding the potential of biomass feedstocks in multiple harvesting sites, availability and seasonality of biomass resources, different potential geographical locations for processing plants that produce multiple products using diverse production technologies, economies of scale for production technologies, demands and prices of multiple products, locations of storage facilities, and a number of transportation modes. Sustainability considerations are incorporated into the proposed models by including simultaneous economic, environmental and social performance in the evaluation of the supply chain designs.

The Water-Energy-Food Nexus: Optimization Models for Decision Making, Volume 32 covers water, energy, and food as crucial resources for human well-being and sustainable development. These resources are inextricably interrelated, therefore, to cover water, energy, and food demands in different sectors and at different scales, it must be considered several sources to produce resources, and the interlinkages of resources for a proper integration must be considered. This book emphasizes issues that must be considered in the design of water-energy-food nexus systems, such as the selection of technologies to produce water or energy, size of technologies, and food required for nutritional demands.In addition, mathematical models are presented for the design of water-energy-food nexus systems involving several strategies to account for issues such as sustainable development, security of resources, interest in conflicts from stakeholders, and efficient allocation of resources.

Machine Learning Tools for Chemical Engineering: Methodologies and Applications examines how machine learning (ML) techniques are applied in the field, offering precise, fast, and flexible solutions to address specific challenges.ML techniques and methodologies offer significant advantages (such as accuracy, speed of execution, and flexibility) over traditional modeling and optimization techniques. This book integrates ML techniques to solve problems inherent to chemical engineering, providing practical tools and a theoretical framework combining knowledge modeling, representation, and management, tailored to the chemical engineering field. It provides a precedent for applied Al, but one that goes beyond purely data-centric ML. It is firmly grounded in the philosophies of knowledge modeling, knowledge representation, search and inference, and knowledge extraction and management.Aimed at graduate students, researchers, educators, and industry professionals, this book is an essential resource for those seeking to implement ML in chemical processes, aiming to foster optimization and innovation in the sector.