a model for the prediction of pollutant species production in the biomass gasification process

Abstract This paper presents a non-stoichiometric equilibrium model for the simulation of biomass downdraft gasifiers. The chemical equilibrium is determined by minimizing the Gibbs free energy. Five elements characterize the biomass and 15 chemical species are considered in the syngas. The model calculates the lower heating value of the syngas and the relative abundances of gasification products. An advantage of this model is that it can easily calculate not only the concentrations of the main gasification products, but also the concentrations of minor product, especially the pollutant chemical species containing Nitrogen and Sulfur. To analyse the model behaviour, a sensitivity analysis on process parameters is presented. The model is validated by comparing its results with the results of simulation carried out with a stoichiometric model and with experimental data found in literature. Finally, the model is applied to the study of the gasification of forest waste

Analysis of the Status of Research and Innovation Actions on Electrofuels under Horizon 2020

Europe stated the ambitious target of becoming carbon neutral by 2050 to combat climate change and meet the requirements imposed by the Paris Agreement, and renewable energy has proved to be a promising solution for the decarbonization of many sectors. Nonetheless, their aleatory nature leads to grid unbalances due to the difference between supply and demand. Storage solutions are needed, and electrofuels become a key factor in this context: they are fuels produced from electricity, which leads to carbon-neutral fuels if it originates from renewable sources. These can constitute a key solution to store the surplus energy and to decarbonize the so-called hard-to-abate sectors. Electrofuel production technologies have not yet been fully developed, and, in this context, extensive study of the state-of-the-art of existing projects can be very useful for researchers and developers. This work researches the European projects funded by the Horizon 2020 Programme regarding electrofuel production. The projects were analyzed in-depth using specific features, and the results were presented

The Status of Research and Innovation on Heating and Cooling Networks as Smart Energy Systems within Horizon 2020

The European Union is funding scientific research through the Horizon 2020 Framework Programme. Since the key priorities for the next few decades are the reduction in carbon emissions and the enhancement of energy system conversion efficiency, a collection of the most recent research projects can be beneficial to researchers and stakeholders who want to easily access and identify recent innovation in the energy sector. This paper proposes an overview of the Horizon 2020 projects on smart distributed energy systems, with particular focus on heating and cooling networks and their efficient management and control. The characteristics of the selected projects are summarized, and the relevant features, including the energy vectors involved, main applications and expected outputs are reported and analyzed. The resulting framework fosters the deployment of digital technologies and software platforms to achieve smart and optimized energy systems

Review on the Status of the Research on Power-to-Gas Experimental Activities

In recent years, power-to-gas technologies have been gaining ground and are increasingly proving their reliability. The possibility of implementing long-term energy storage and that of being able to capture and utilize carbon dioxide are currently too important to be ignored. However, systems of this type are not yet experiencing extensive realization in practice. In this study, an overview of the experimental research projects and the research and development activities that are currently part of the power-to-gas research line is presented. By means of a bibliographical and sitographical analysis, it was possible to identify the characteristics of these projects and their distinctive points. In addition, the main research targets distinguishing these projects are presented. This provides an insight into the research direction in this regard, where a certain technological maturity has been achieved and where there is still work to be done. The projects found and analyzed amount to 87, mostly at laboratory scale. From these, what is most noticeable is that research is currently focusing heavily on improving system efficiency and integration between components

a model for filter diagnostics in a syngas fed chp plant

Abstract Biomass gasification is an important opportunity for power generation and combined heat and power (CHP), as it allows for biomass use in high efficiency, low emissions energy systems, e.g., internal combustion engines. Biomass-based CHP is particularly interesting for the service sector, as it allows to use a programmable renewable energy source to produce both electricity and heat, unlike photovoltaic systems which are typically used in this sector. Yet, small-scale gasification and CHP systems have a poor diffusion, due to a lack of acknowledged reliability. To improve reliability and performance, accurate simulation models may be useful, in particular for system control and diagnosis. For this purpose, the project SYNBIOSE proposes the installation, testing and simulation of a commercial-grade system for the gasification of lignocellulosic woodchips and pellets coupled to CHP in the campus of the University of Parma. One of the project deliverables is a simulation model of the whole gasification and CHP plant, for system diagnosis. The model has a modular structure (to allow for improvements and applications) and is implemented in MATLAB®/Simulink®. The present work focuses on syngas filters, which are among the most critical components. The outcome is a model able to predict the operation of filters taking into account inlet gas characteristics and fouling. Model analysis, sensitivity analysis and validation showed that simulation outputs are consistent with the physical behavior and experimental data. The model proved to be useful for system and components simulation and diagnosis

Development and Application of Co-simulation and "Control- oriented" Modeling in the Improvement of Performance and Energy Saving of Mobile Machinery☆

Abstract Due to rising energy costs and tighter emissions restrictions from law regulations, mobile machinery and off-road vehicles manufacturers are forced to develop and exploit new techniques for the reduction of fuel consumption and pollutant emission. The main focus in this direction is the optimization of the matching between the fluid power circuit and the thermal engine to improve the efficiency of the hydraulic system and reducing the fuel consumption. A specific research activity has been started in this field by the authors to define methods and techniques for the mathematical simulation of off-road vehicles, where usually hydraulic systems are powered by internal combustion engines. The models proposed in the paper and the related results clearly show how these simulation tools can be used to improve the energy efficiency of the overall system, leading to an interesting reduction in fuel consumption by merely changing the engine rotational speed instead of adopting a constant-speed strategy

a library for the simulation of smart energy systems the case of the campus of the university of parma

Abstract Smart energy systems are complex systems (i.e. composed of windmills, PV panels, solar collectors, heat pumps, CHP systems, etc) in which synergies rise through the ICT (Information and Communications Technology) based management and control of the whole system. In the development of efficient smart energy systems, a fundamental step is the optimization of total energy conversion, transmission and utilization processes within the whole system. To this extent, mathematical models can represent very useful tools for the simulation of the behavior of the system. In this paper, a library for the dynamic simulation of smart energy systems is presented. The library is implemented in Matlab ® /Simulink ® and each component (i.e. the energy conversion and distribution systems and the end-users) is developed through a modular approach. Therefore, the modules are designed by considering a standardized input/output and causality structure. Finally, the capabilities of this approach are evaluated through the application to the district heating and cooling network of the Campus of the University of Parma. The case study is based on a branch which feeds twelve buildings with a total heating volume of about 150 000 m 3 and peak thermal power demand of about 8 MW. Results reported in the paper demonstrate the effectiveness of this approach and the capability in term of system optimization

Coupling excavator hydraulic system and internal combustion engine models for the real-time simulation

Rising energy costs and emissions restrictions force manufacturers to exploit new techniques to reduce fuel consumption and pollutant production. Many solutions have been proposed for off-road vehicles, mainly based on reduction of hydraulic losses, better control strategies and introduction of hybrid architectures. In these applications the optimization of the matching between hydraulic system and thermal engine is a major concern to improve system overall efficiency. The work presented in the paper is focused on the development of a method for the simulation of typical mobile machinery where hydraulic systems are powered by internal combustion engines; the proposed co-simulation approach can be useful in the development cycle of this machinery

Optimization of Load Allocation Strategy of a Multi-source Energy System by Means of Dynamic Programming

AbstractMulti-source systems for the fulfillment of electric, thermal and cooling demand of a building can be based on different technologies (e.g. solar photovoltaic, solar heating, cogeneration, heat pump, absorption chiller) which use renewable, partially renewable and fossil energy sources. The main issues of these kinds of multi-source systems are (i) the allocation strategy which allows the division of the energy demands among the various technologies and (ii) the proper sizing of each technology.Furthermore, these two issues proves to be deeply interrelated because, while a wiser energy demand allocation strategy can lead to significant reductions in primary energy consumption, the definition itself of an optimal allocation strategy strongly depends on the actual sizing of the employed technologies. Thus the problem of optimizing the sizing of each technology cannot be separated from the definition of an optimal control strategy. For this purpose a model of a multi-source energy system, previously developed and implemented in the Matlab® environment, has been considered. The model takes account of the load profiles for electricity, heating and cooling for a whole year and the performance of the energy systems are modelled through a systemic approach. A dynamic programming algorithm is therefore employed in order to obtain an optimal control strategy for the energy demand allocation during the winter period. While the resulting control strategy is non-causal and therefore not suitable for the implementation on a real-time application, it allows the definition of a benchmark on the maximum primary energy savings achievable with a specific sizing solution. This result is therefore very helpful both in comparing different solutions and in subsequently define a proper causal control strategy. Finally, the model is applied to the case of a thirteen-floors tower composed of a two-floor shopping mall at ground level and eleven floors used as offices

Predictive Controller for Refrigeration Systems Aimed to Electrical Load Shifting and Energy Storage

The need to reduce greenhouse gas emissions is leading to an increase in the use of renewable energy sources. Due to the aleatory nature of these sources, to prevent grid imbalances, smart management of the entire system is required. Industrial refrigeration systems represent a source of flexibility in this context: being large electricity consumers, they can allow large-load shifting by varying separator levels or storing surplus energy in the products and thus balancing renewable electricity production. The work aims to model and control an industrial refrigeration system used for freezing food by applying the Model Predictive Control technique. The controller was developed in Matlab® and implemented in a Model-in-the-Loop environment. Two control objectives are proposed: the first aims to minimize total energy consumption, while the second also focuses on utilizing the maximum amount of renewable energy. The results show that the innovative controller allows energy savings and better exploitation of the available renewable electricity, with a 4.5% increase in its use, compared to traditional control methods. Since the proposed software solution is rapidly applicable without the need to modify the plant with additional hardware, its uptake can contribute to grid stability and renewable energy exploitation