IFAC PapersOnLine 51-5 (2018) 114–119 ScienceDirect Available online at www.sciencedirect.com 2405-8963 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Peer review under responsibility of International Federation of Automatic Control. 10.1016/j.ifacol.2018.06.220 © 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: biomass, gasification, optimization, decision support system. 1. INTRODUCTION The use of renewable sources is more than ever a current issue to deal with because of the need to reduce greenhouse gas emissions, in particular referring to carbon dioxide. Several technologies have been developed to exploit different energy sources. In this paper, attention is focused on forest biomass and gasification plants. In particular, a decision support system for the optimal design of a gasification plant and the related supply chain is proposed. In literature, several works deal with the optimal design of plants that use biomasses. A mathematical optimization model for the optimal design of industrial furnaces/fired heaters is presented by Mussati et al. (2009). Duret et al. (2005) consider the optimal thermo-economic process design for thermo-chemical fuel production from biomass: by solving a linear programming problem, the optimal process layout and its corresponding utility system is determined minimizing operating cost. Shabani and Sowlati (2013) develop a dynamic optimization model to define the amount of biomass to be purchased, stored and consumed in each month during a one-year planning horizon, taking into account different types of forest fuel, several storage options, energy production, ash management and several time periods. Frombo et al. (2009) develop a geographic information system (GIS)-based Environmental Decision Support System for the optimal planning of forest biomass use for energy production. The decision variables are related to plant locations, conversion processes (pyrolysis, gasification, combustion), harvested biomass. Shahi et al. (2011) propose an integrated non-linear dynamic mixed-integer programming model for biomass gasification power plants. The main variables considered in the model are harvesting and processing costs, logistics costs for biomass feedstock delivery and storage, capital costs of the power plant, operation and maintenance costs including labour, insurance, and capital financing, and other regulatory costs. Muresan et al. (2013) develop a model that assesses the effect of biomass co-firing on gasification-based hydrogen production supply chain, with carbon dioxide capture and storage. The proposed paper, differently from literature, focuses attention on each component of a gasification plant and to its overall supply chain. Concerning previous works of the research group (for example the one reported in Frombo et al. 2009), a completely different, more complex, model is used for the gasification plant. Moreover, storage systems and pellets production have been considered. The resulting optimization problem is non-linear mixed-integer. The paper is organized as follows: section 2 describes the system model, while section 3 describes the optimization problem. Then, results and conclusions are reported in sections 4 and 5, respectively. 2. THE CONSIDERED SYSTEM The considered system is characterized by three main sub-systems, as reported in Fig. 1: the biomass supply and pre-treatment, the pellets’ production, and the power production. Each of them is in turn characterized by several sub-systems. For this reason, for the sake of brevity, the main features of the system model are here described, but the overall formalization is omitted. The objective is the one of sizing the supply chain of a gasification plant with known capacity for power production. The decision variables are related to: storage systems capacity, number of technologies, amount of biomass treated in each production line, humidity, heat flows, type pf co-generation technology. All the wooden biomass can be purchased from different suppliers and can be constituted by virgin wood, harvested directly in the forest area, or by sub-products of other manufacturing processes, such as pruning, mowing, and cutting, agricultural residuals, public green maintenance. The first kind of biomass is characterized by a higher specific cost Abstract: The exploitation of biomass in power production systems is particularly attractive to reduce CO 2 emissions. However, the installation of biomass-fed power plants is not always technically and economically competitive with respect to traditional fossil fuels. This paper aims to provide a decision support system that can help the decision maker in assessing the feasibility of the plant, from a technical and economic point of view, and to provide its optimal design. The model is adapted for a plant that can produce pellets, electricity, and heat, through the gasification process. The developed model is applied to a real case study in the Alessandria Province (Italy). * Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, via Opera Pia 13, 16145, Italy, e-mail: michela.robba@unige.it **Demont s.r.l., Via Braia, 21, 17017 Millesimo, Savona, Italy G. Ferro*, R. Minciardi*, E. Podestà**, M. Robba* An optimization model for the sizing of the biomass plants’ supply chain