Substitution of fossil gaseous fuels with biomass-based gases is of interest to the iron and steel industry due to its role in the mitigation of anthropogenic CO2emissions. In switching from fossil fuels to biomass-based gases, a systems analysis of the full value chain from biomass supply to the production and supply of final gas products becomes crucial. This study uses process and heat integration methods in combination with a supply chain evaluation to analyse full value chains of biomass-based gases for fossil gas replacement within the iron and steel industry. The study is carried out as a specific case study in order to understand the implications of utilizing bio-syngas/bio-SNG as heating fuels in iron- and steel-making, and to provide insights into the most sensitive parameters involved in fuel switching. The results show a significant cost difference in the fuel production of the two gas products owing to higher capital and biomass use in the bio-SNG value chain option. When tested for sensitivity, biomass price, transportation distance, and capital costs show the most impact on fuel production costs across all options studied. Trade-offs associated with process integration, plant localisation, feedstock availability and supply were found to varying extents.

While previous research has focused on single industrial sectors or specific technologies, this study aims to explore the impacts of various industrial technology options on the use of biomass and electricity in a future fossil free Swedish industry. By building a small optimization model, that decomposes each industrial sector into site categories by type and technology to capture critical synergies among industrial processes. The results show important synergies between electrification, biomass and CCS/U (sequestration of CO₂ is required to reach net-zero emissions). Reaching an absolute minimum of biomass use within the industry has a very high cost of electricity due to the extensive use of power-to-gas technologies, and minimising electricity has a high cost of biomass due to extensive use of CHP technologies. Meanwhile, integrated bio-refinery processes are the preferable option when minimising the net input of energy. There is, thus, no singular best technology, instead the system adapts to the given circumstances showing the importance of a detailed bottom-up modelling approach and that the decarbonisation of the industry should not be treated as a site-specific problem, but rather as a system-wide problem to allow for optimal utilisation of process synergies.

Traditional fossil fueled power plants are commonly based on steam Rankine cycle or Brayton Joule cycle. Using water or air as working fluid is obviously the most obvious choice for the wide availability of these substances in nature. However, the scarcity of natural energy sources and the strong need of reducing environmental impact have necessarily drawn the research to new energy systems configurations operating with other working fluids, which are able to recover lower temperature sources, such as Sun or industrial wasted heat. The variety of new working fluids (refrigerants or organic fluids) widens the choice to a variety of configurations that can be tailored to the specific source characteristics and boundary constraints. It is not always easy or even possible to conceive the best configuration for given specifications with the mere experience of a common designer. To design a new system configuration, the designer normally uses some “non-codified rules” deriving from his knowledge of basic thermodynamics and energy engineering. This paper aims instead at showing a practical tool that is based on a new methodology, named SYNTHSEP, to generate new energy system configurations. This methodology starts from the simple thermodynamic cycles operated by a given fluid made up of the four fundamental processes (compression, heating, expansion and cooling) and uses a rigorous set of codified rules to build the final system configuration. The paper presents the basics of the new methodology and how it has been implemented in a practical tool that simply requires the information about the elementary cycles and their shared processes as input data.

Producer gas from biomass gasification contains impurities like tars, particles, alkali salts, and sulfur/nitrogen compounds. As a result, a number of process steps are required to condition the producer gas before utilization as a syngas and further upgrading to final chemicals and fuels. Here, we study the concept of using molten carbonate electrolysis cells (MCEC) both to clean and to condition the composition of a raw syngas stream, from biomass gasification, for further upgrading into synthetic natural gas (SNG). A mathematical MCEC model is used to analyze the impact of operational parameters, such as current density, pressure and temperature, on the quality and amount of syngas produced. Internal rate of return (IRR) is evaluated as an economic indicator of the processes considered. Results indicate that, depending on process configuration, the production of SNG can be boosted by approximately 50-60% without the need of an additional carbon source, i.e., for the same biomass input as in standalone operation of the GoBi-Gas plant. Copyright

This Special Issue addresses the general problem of a proper match between the demands of energy users and the units for energy conversion and storage, by means of proper design and operation of the overall energy system configuration. The focus is either on systems including single plants or groups of plants, connected or not to one or more energy distribution networks. In both cases, the optimum design and operation involve decisions about thermodynamic processes, about the type, number, design parameters of components/plants, and storage capacities, and about mutual interconnections and the interconnections with the distribution grids. The problem is very wide, can be tackled with different methodologies and may have several, more or less valuable and complicated solutions. The twelve accepted papers certainly represent a good contribution to perceive its difficulty.

The fundamental challenge in the synthesis/design optimization of energy systems is the definition of system configuration and design parameters. The traditional way to operate is to follow the previous experience, starting from the existing design solutions. A more advanced strategy consists in the preliminary identification of a superstructure that should include all the possible solutions to the synthesis/design optimization problem and in the selection of the system configuration starting from this superstructure through a design parameter optimization. This top–down approach cannot guarantee that all possible configurations could be predicted in advance and that all the configurations derived from the superstructure are feasible. To solve the general problem of the synthesis/design of complex energy systems, a new bottom–up methodology has been recently proposed by the authors, based on the original idea that the fundamental nucleus in the construction of any energy system configuration is the elementary thermodynamic cycle, composed only by the compression, heat transfer with hot and cold sources and expansion processes. So, any configuration can be built by generating, according to a rigorous set of rules, all the combinations of the elementary thermodynamic cycles operated by different working fluids that can be identified within the system, and selecting the best resulting configuration through an optimization procedure. In this paper, the main concepts and features of the methodology are deeply investigated to show, through different applications, how an artificial intelligence can generate system configurations of various complexity using preset logical rules without any “ad hoc” expertise.

Thermoacoustic instabilities usually result from the coupling between the oscillatory heat release and one or more natural acoustic modes of the combustion system. When the shifting of system frequencies caused by the unsteady heat release is limited, the calculation of natural modes allows to identify which of them are excited by the flame once changes in flow temperature and composition due to combustion are considered. In this paper, isothermal computational fluid dynamics simulations are performed to predict the natural modes of a heavy-duty gas turbine combustor in reactive conditions. Combustion and heat transfer are neglected in the numerical analysis to simplify the model and limit the computational effort. The natural frequencies resulting from isothermal simulations are then corrected using a rather basic post-processing approach to account for temperature and gas composition changes due to combustion process. Frequency and amplitude of the calculated modes are finally compared to experimental measurements to evaluate the ability of the acoustic analysis to capture frequency and spatial shape of the combustor natural modes excited by the flame

The urbanization of new areas beyond the existing perimeter of a town implies the expansion of several infrastructures, including the district heating network. The main variables involved in the design of the district heating network expansion are the layout of the new pipes, their diameters, and the capacity of the new heat production sites that are required to satisfy the increased demand of room heating and hot tap water. In this paper, a multi-objective evolutionary algorithm is applied to the minimization of the costs related to the expansion of the district heating network of the town of Kiruna, in northern Sweden. The results show that the spectrum of the optimal design compromises between investment costs for the new pipes and the new heat generation site on one side, and operating costs due to overall fuel consumption and pumping power in the network on the other. The presented methodology is a tool meant for the decision makers in the company who own the district heating network, to evaluate all the possible best design alternatives before making a decision.

Producer gas from biomass gasification contains impurities like tars, particles, alkali salts and sulfur/nitrogen compounds. As a result a number of process steps are required to condition the producer gas before utilization as a syngas and further upgrading to final chemicals and fuels. Here, we study the concept of using molten carbonate electrolysis cells (MCEC) both to clean and to condition the composition of a raw syngas stream, from biomass gasification, for further upgrading into SNG. A mathematical MCEC model is used to analyze the impact of operational parameters, such as current density, pressure and temperature, on the quality and amount of tailored syngas produced. Investment opportunity is evaluated as an economic indicator of the processes considered. Results indicate that the production of SNG can be boosted by approximately 50% without the need of an additional carbon source, i.e. for the same biomass input as in standalone operation of the GoBiGas plant.