• Energy Research

Abstract The legislation of various European countries imposes limits on the demand for building heating and cooling in order to reduce the primary energy consumptions. Moreover, the legislation prescribes that a fraction of the demand for building cooling, heating and power must be met through renewable energy sources. Among renewable energy systems, wind power, solar photovoltaic, solar thermal energy, solar cooling and heat pumps (though only “partially” renewable) have to be mentioned. In this framework combined heat and power (CHP) systems can provide a further solution to reduce the primary energy consumption. Due to the availability of different technologies, a key factor is the choice of the allocation strategy which allows the division of the energy demands among the various technologies in order to minimize the primary energy consumption. Since the cost of the technologies and the actual tariff and incentive scenarios depend on the specific country and may lead to not optimal allocation strategies in terms of primary energy consumption, these economic parameters are not taken into consideration in the analysis. Therefore, the obtained solutions represent a target which the policies should aim to achieve. This paper aims to develop and apply a methodology for the optimal allocation of the demand among CHP and renewable energy systems, with the aim of minimizing the primary energy consumption, by accounting for legislative constraints. The methodology is then applied to different climatic scenarios to evaluate the effects of a variation of the demand and technology characteristics on the allocation of the loads. Moreover, an analysis on the combined effects is presented. Finally, some guidelines are obtained.

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.

Solid particle ingestion is one of the principal degradation mechanisms in the turbine and compressor sections of gas turbines. In particular, in industrial applications, the micro-particles not captured by the air filtration system cause fouling and, consequently, a performance drop of the compressor. This paper presents three-dimensional numerical simulations of the micro-particle ingestion (0–2 μm) on an axial compressor rotor carried out by means of a commercial computational fluid dynamic code. Particles of this size can follow the main air flow with relatively little slip, while being impacted by flow turbulence. It is of great interest to the industry to determine which areas of the compressor airfoils are impacted by these small particles. Particle trajectory simulations use a stochastic Lagrangian tracking method that solves the equations of motion separate from the continuous phase. Then, the NASA Rotor 37 is considered as a case study for the numerical investigation. The compressor rotor numerical model and the discrete phase treatment have been validated against the experimental and numerical data available in literature. The number of particles, sizes, and concentrations are specified in order to perform a quantitative analysis of the particle impact on the blade surface. The results show that micro-particles tend to follow the flow by impacting at full span with an higher impact concentration on the pressure side. The suction side is affected only by the impact of the smaller particles (up to 1 μm). Particular fluid-dynamic phenomena such as separation, stagnation point and tip leakage vortex strongly influence the impact location of the particles.

Solid particle ingestion is one of the principal degradation mechanisms in the turbine and compressor sections of gas turbines. In particular, in industrial applications, the microparticles that are not captured by the air filtration system cause fouling and, consequently, a performance drop of the compressor. This paper presents three-dimensional numerical simulations of the microparticle ingestion (0 μm–2 μm) on an axial compressor rotor carried out by means of a commercial computational fluid dynamic (CFD) code. Particles of this size can follow the main air flow with relatively little slip, while being impacted by flow turbulence. It is of great interest to the industry to determine which areas of the compressor airfoils are impacted by these small particles. Particle trajectory simulations use a stochastic Lagrangian tracking method that solves the equations of motion separate from the continuous phase. Then, the NASA Rotor 37 is considered as a case study for the numerical investigation. The compressor rotor numerical model and the discrete phase treatment have been validated against the experimental and numerical data available in literature. The number of particles, sizes, and concentrations are specified in order to perform a quantitative analysis of the particle impact on the blade surface. The results show that microparticles tend to follow the flow by impacting at full span with a higher impact concentration on the pressure side (PS). The suction side (SS) is affected only by the impact of the smaller particles (up to 1 μm). Particular fluid dynamic phenomena, such as separation, stagnation point, and tip leakage vortex, strongly influence the impact location of the particles.

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.

Over recent decades, the variability and high costs of the traditional gas turbine fuels (e.g., natural gas) have pushed operators to consider low-grade fuels for running heavy-duty frames. Synfuels, obtained from coal, petroleum, or biomass gasification, could represent valid alternatives in this sense. Although these alternatives match the reduction of costs and, in the case of biomass sources, would potentially provide a CO2 emission benefit (reduction of the CO2 capture and sequestration costs), these low-grade fuels have a higher content of contaminants. Synfuels are filtered before the combustor stage, but the contaminants are not removed completely. This fact leads to a considerable amount of deposition on the nozzle vanes due to the high temperature value. In addition to this, the continuous demand for increasing gas turbine efficiency determines a higher combustor outlet temperature. Current advanced gas turbine engines operate at a turbine inlet temperature (TIT) of (1400–1500) °C, which is high enough to melt a high proportion of the contaminants introduced by low-grade fuels. Particle deposition can increase surface roughness, modify the airfoil shape, and clog the coolant passages. At the same time, land-based power units experience compressor fouling, due to the air contaminants able to pass through the filtration barriers. Hot sections and compressor fouling work together to determine performance degradation. This paper proposes an analysis of the contaminant deposition on hot gas turbine sections based on machine nameplate data. Hot section and compressor fouling are estimated using a fouling susceptibility criterion. The combination of gas turbine net power, efficiency, and TIT with different types of synfuel contaminants highlights how each gas turbine is subjected to particle deposition. The simulation of particle deposition on 100 gas turbines ranging from 1.2 MW to 420 MW was conducted following the fouling susceptibility criterion. Using a simplified particle deposition calculation based on TIT and contaminant viscosity estimation, the analysis shows how the correlation between type of contaminant and gas turbine performance plays a key role. The results allow the choice of the best heavy-duty frame as a function of the fuel. Low-efficiency frames (characterized by lower values of TIT) show the best compromise in order to reduce the effects of particle deposition in the presence of high-temperature melting contaminants. A high-efficiency frame is suitable when the contaminants are characterized by a low-melting point thanks to their lower fuel consumption.

Centrifugal compressor impellers and shafts are subject to severe fluctuating axial and radial forces when operating in surge. These forces can cause severe damage to the close clearance components of a centrifugal compressor such as the thrust and radial bearings, inter-stage and dry gas seals, and balance piston. Being able to accurately quantify the cyclic surge forces on the close clearance components of the compressor allows the user to determine whether an accidental surge event, or emergency shutdown (ESD) transient, has caused damage requiring inspection, repair, or part replacement. For the test, a 700 Hp (∼520 kW) industrial air centrifugal compressor was operated in surge at speeds ranging from 7,000 to 13,000 rpm and pressure ratios from 1.2 to 1.8. The axial surge forces were directly measured using axial load cells on the thrust bearings. Suction and discharge pressures, proximity probe axial shaft position, flows, and temperatures were also measured. Time domain and frequency plots of axial vibration and dynamic pulsations showed the impact of the operating conditions on surge force amplitudes and frequencies. A surge severity coefficient was also derived as a simple screening tool to evaluate the magnitude of potential damage to a compressor during surge.

This paper documents the setup and validation of nonlinear autoregressive network with exogenous inputs (NARX) models of a heavy-duty single-shaft gas turbine (GT). The data used for model training are time series datasets of several different maneuvers taken experimentally on a GT General Electric PG 9351FA during the start-up procedure and refer to cold, warm, and hot start-up. The trained NARX models are used to predict other experimental datasets, and comparisons are made among the outputs of the models and the corresponding measured data. Therefore, this paper addresses the challenge of setting up robust and reliable NARX models, by means of a sound selection of training datasets and a sensitivity analysis on the number of neurons. Moreover, a new performance function for the training process is defined to weigh more the most rapid transients. The final aim of this paper is the setup of a powerful, easy-to-build and very accurate simulation tool, which can be used for both control logic tuning and GT diagnostics, characterized by good generalization capability.