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Abstract The combined production of electricity, heat and cold by a polygeneration system connected to a district heating and cooling network can provide high energy utilization efficiency. The inherent complexity of simultaneous production of different services and the high variability in the energy demand make combined cooling and heating systems performance highly dependent on the operational strategy. In this paper, an operational optimization method based on the moving average of real-time measurements of energy demands and ambient conditions is proposed. Real energy demand data from a district heating and cooling network close to Barcelona, Spain, are used to test the method. A complex polygeneration system is considered, consisting of an internal combustion engine, a double-effect absorption chiller, an electric chiller, a boiler and a cooling tower. A detailed modelling of the system is provided, considering partial load behavior of the components and ambient conditions effects. Results of the real-time optimal management are discussed and compared to traditional operational strategies and to the ideal optimal management achievable with perfectly accurate forecast of energy demands. Moreover, the optimal width of the window adopted for the moving average of real-time data is identified.

Electrical power grid insulators installed outdoors are exposed to environmental conditions, such as the accumulation of contaminants on their surface. The contaminants increase the surface conductivity of the insulators, increasing leakage current until there is a flashover. Evaluating the increase in leakage current in relation to the contamination level is one way to determine the insulation condition. This paper evaluates a time series of leakage current from a high-voltage laboratory experiment using porcelain pin-type insulators. Time series forecasting is performed with a collection of machine learning models known as ensemble learning approaches, which include blending, bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization. According to this paper’s findings, applying these ensemble learning approaches is useful for enhancing the performance of the machine learning models in forecasting the occurrence of breakdowns in the electrical power system. The Hodrick–Prescott filter reduces the root mean square error performance metric (to be minimized) by 2.69 times using the ensemble random subspace approach. According to the results of this paper, the proposed method is stable, with low variance when a statistical analysis is performed, being superior to the long short-term memory neural network.

Abstract This paper summarizes observations and ideas on cellulose pyrolysis products and structural studies on cellulose chars with a focus on conceptual integration. Information is presented on the larger gas phase oligosaccharides in cellulose pyrolysates, their mechanism of formation and the implications for the processes in the initial stages of char formation. A model for the formation of a new thermally synthesized three-dimensional network polymer is proposed.

Abstract In this work, Co/Cu nanowires are fabricated by pulsed electrodeposition from a single bath solution containing both Co and Cu ions. Alternate Co and Cu layers are deposited into the nanopores of track etched polycarbonate templates. Although the feasibility of this process is generally recognized, some important issues such as process reproducibility and how structural defects affect the nanowires arrays’ sensing performances are still open; conditions necessary to turn a this made system into a magnetic field sensor. The present work aims at pushing forward knowledge concerning the nanowires fabrication and defining the best growth parameters; in particular, a tight control of the growth process parameters such as single metal deposition potentials and single cycle deposition durations have been carried out for nanowires of 80 nm diameter and correlated to the system magneto-electric response.

The Flameless Combustion (FC) regime has been pointed out as a promising combustion technique to lower the emissions of nitrogen oxides (NOx) while maintaining low CO and soot emissions, as well as high efficiencies. However, its accurate modeling remains a challenge. The prediction of pollutant species, especially NOx, is affected by the usually low total values that require higher precision from computational tools, as well as the incorporation of relevant formation pathways within the overall reaction mechanism that are usually neglected. The present work explores a multiple step modeling approach to tackle these issues. Initially, a CFD solution with simplified chemistry is generated [both the Eddy Dissipation Model (EDM) as well as the Flamelet Generated Manifolds (FGM) approach are employed]. Subsequently, its computational cells are clustered to form ideal reactors by user-defined criteria, and the resulting Chemical Reactor Network (CRN) is subsequently solved with a detailed chemical reaction mechanism. The capabilities of the clustering and CRN solving computational tool (AGNES—Automatic Generation of Networks for Emission Simulation) are explored with a test case related to FC. The test case is non-premixed burner based on jet mixing and fueled with CH4 tested for various equivalence ratios. Results show that the prediction of CO emissions was improved significantly with respect to the CFD solution and are in good agreement with the experimental data. As for the NOx emissions, the CRN results were capable of reproducing the non-monotonic behavior with equivalence ratio, which the CFD simulations could not capture. However, the agreement between experimental values and those predicted by CRN for NOx is not fully satisfactory. The clustering criteria employed to generate the CRNs from the CFD solutions were shown to affect the results to a great extent, pointing to future opportunities in improving the multi-step procedure and its application.

Abstract We evaluate the contribution of technological change in reducing CO 2 emissions in the Italian pulp and paper industry during the first and second phases of application of the European Union Emission Trading System (EU-ETS). We decompose the variation in emission and emission intensity into three different types of effects: a composition effect, a technique effect and a scale effect. The composition effect measures the change in emissions and emissions intensity due to a shift in production towards products that cause less emissions. The technique effect measures the change per each type of product, thereby accounting for technology improvements in the production of each type of good produced. The scale effect singles out the reduction in total emission due to an overall reduction in output. We show that the first phase of the application of EU-ETS has led to a reduction in both emissions and emission intensity due to the composition effect. The technological change has had a limited negative impact on emissions in the first phase, while in the second phase there has been limited technology improvement in the industry. However, the figures of the scale effect show that the larger reduction in emission is due to the overall decrease in output.

A hardware-in-the-loop-simulation (HiLS) procedure for a direct-fired fuel cell turbine hybrid power system was evaluated for an integrated gasifier/fuel cell/turbine hybrid cycle (IGFC), implemented through the Hybrid Performance (Hyper) project at the National Energy Technology Laboratory, U.S. Department of Energy (NETL). The Hyper facility is designed to explore dynamic operation of hybrid systems and quantitatively characterize such transient behaviour. It is possible to model, test and evaluate the effects of different parameters on the design and operation of a gasifier/fuel cell/gas turbine hybrid system and quantify risk mitigation strategies. The previous implementation of emergency shut-down control strategies resulted in turbomachinery hardware failure. The primary linking event in these cases was compressor stall and surge resulting from the sudden loss of fuel during implementation of the standard double block and bleed strategy used during emergency failure. A new mitigation strategy involving automated ramps is proposed and described in detail to control the system from start-up to forced emergency shut-down. The control architecture shows how the virtual fuel cell model can be coupled to the real gas turbine safely, in all of stage of operations. The paper includes improvements to the emergency shutdown procedure, failure analyses, and the comparison of experimental data with previous results.