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This article is categorized under: Energy Research & Innovation > Science and Materials Energy Research & Innovation > Economics and Policy Energy Research & Innovation > Systems and Infrastructure Energy Research & Innovation > Climate and Environment

Industrial producers face the task of optimizing production process in an attempt to achieve the desired quality such as mechanical properties with the lowest energy consumption. In industrial carbon fiber production, the fibers are processed in bundles containing (batches) several thousand filaments and consequently the energy optimization will be a stochastic process as it involves uncertainty, imprecision or randomness. This paper presents a stochastic optimization model to reduce energy consumption a given range of desired mechanical properties. Several processing condition sets are developed and for each set of conditions, 50 samples of fiber are analyzed for their tensile strength and modulus. The energy consumption during production of the samples is carefully monitored on the processing equipment. Then, five standard distribution functions are examined to determine those which can best describe the distribution of mechanical properties of filaments. To verify the distribution goodness of fit and correlation statistics, the Kolmogorov-Smirnov test is used. In order to estimate the selected distribution (Weibull) parameters, the maximum likelihood, least square and genetic algorithm methods are compared. An array of factors including the sample size, the confidence level, and relative error of estimated parameters are used for evaluating the tensile strength and modulus properties. The energy consumption and N2 gas cost are modeled by Convex Hull method. Finally, in order to optimize the carbon fiber production quality and its energy consumption and total cost, mixed integer linear programming is utilized. The results show that using the stochastic optimization models, we are able to predict the production quality in a given range and minimize the energy consumption of its industrial process.

Chemical looping combustion allows the carbon dioxide capture by using an oxygen carrier, which transports the oxygen required for combustion from the air to the fuel. But complete combustion of a solid fuel is not achieved when low cost materials were used as oxygen carriers. Manganese‑iron mixed oxide doped with titanium has been identified as a promising oxygen carrier to improve combustion efficiency due to its oxygen uncoupling capability. The objective of this work was to assess the potential of this oxygen carrier when burning coal in a chemical looping unit. The coal combustion efficiency and carbon dioxide capture were evaluated as a function of the operating conditions both in the fuel and air reactor. Carbon dioxide capture was affected by the solids residence time in the fuel reactor. Coal combustion efficiency increased as the oxygen uncoupling capability was enhanced by using suitable operating conditions in the air reactor. Almost full coal combustion (99.4%) was achieved by setting an air reactor temperature of 880 °C, an air excess of 1.8, a fuel reactor temperature of 925 °C, and an oxygen carrier to fuel ratio >3. The oxygen carrier showed magnetic properties, allowing its re-use after being separated from ash. This work was partially supported by the project ENE2016-77982-R (AEI/FEDER, UE) and project ENE2017-89473R (AEI/FEDER, UE), and by the Consejo Superior de Investigaciones Científicas (CSIC project: 2017-80E035). 10 Figures, 4 Tables.-- © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ Peer reviewed

Abstract A LOWTRAN 5 based flux model has been developed to calculate downwelling infrared irradiance from a clear night atmosphere onto a horizontal or tilted surface. This model is based on the transmittance/radiance code LOWTRAN 5 which can calculate the radiance from the atmosphere for user defined paths, atmospheric conditions and spectral intervals. Included in the model is the addition of a zeroth order scattering approximation to the LOWTRAN 5 code, methods of integrating LOWTRAN 5 calculated radiances over the sky hemisphere to obtain the downwelling flux, and a method for calculating the radiance from the atmosphere at wavenumbers outside the range of LOWTRAN 5. The accuracy of this model is verified by comparison of calculations based on radiosonde data with surface flux measurements taken concurrent with radiosonde ascent. Agreement is excellent for both horizontal and tilted surfaces with the deviation between measurements and calculations of the flux on a horizontal surface being less than 4 per cent.

We present the results of the spectral transmittance and reflectance of a SiO2/TiO2 1D photonic crystal deposited by Radio Frequency sputtering on a flexible polymeric substrate, which shows a blue-shift in its transmittance stopband proportional to the incidence angle when bent. An adjustable sample holder was designed to regulate the bending and keep the sample in a bent condition. Different angles of incidence were also achieved through variable angle reflectance, where an increase in incidence angle led to the blue-shift and narrowing of the transmittance stopband. We addressed the sample's resistance against bending wear and tear by comparing the transmittance spectra acquired for the flat sample before and after the measurements made in bent configurations. An important stability has been observed.

Abstract Solar energy and wind power supply are renewable, decentralised and intermittent electrical power supply methods that require energy storage. Integrating this renewable energy supply to the electrical power grid may reduce the demand for centralised production, making renewable energy systems more easily available to remote regions. Control systems optimise solar energy and wind power sources to supply renewable energy to the power grid. Vehicle to Grid (V2G) operations support intermittent production as battery storage. In V2G operations, electric power flows from the power grid to the battery storage and from the battery storage back to the power grid. The primary goal of this study is to improve the existing renewable energy supply to provide more reliable units in the power grid. We consider the V2G concept as an extension of the smart charging system allowing electric vehicles to be able to inject battery energy into the power grid, acting as distributed generators or energy storage systems. This review shows how parallel V2G storage and battery storage supports the power grid. Further, the review indicates that decentralised V2G battery storages will be included in future renewable energy systems.

With the increasing complexity of modern power systems, conventional dynamic load modeling with ZIP and induction motors (ZIP + IM) is no longer adequate to address the current load characteristic transitions. In recent years, the WECC composite load model (WECC CLM) has shown to effectively capture the dynamic load responses over traditional load models in various stability studies and contingency analyses. However, a detailed WECC CLM model typically has a high degree of complexity, with over one hundred parameters, and no systematic approach to identifying and calibrating these parameters. Enabled by the wide deployment of PMUs and advanced deep learning algorithms, proposed here is a double deep Q-learning network (DDQN)-based, two-stage load modeling framework for the WECC CLM. This two-stage method decomposes the complicated WECC CLM for more efficient identification and does not require explicit model details. In the first stage, the DDQN agent determines an accurate load composition. In the second stage, the parameters of the WECC CLM are selected from a group of Monte-Carlo simulations. The set of selected load parameters is expected to best approximate the true transient responses. The proposed framework is verified using an IEEE 39-bus test system on commercial simulation platforms. To appear in IEEE Transactions on Smart Grid