• Energy Research
• Open Access close
• Closed Access close
• Restricted close
• Open Source close
• AU close

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.

<p>This industry-oriented paper presents an overview and in-depth analysis of previous and current situations of power system frequency response and control in Australia. The evaluation of different services provided by different electricity market players under the supervision of the Australian Energy Market Operator (AEMO) as an independent system operator provides lessons and an understanding of the current operation status with its real challenges and opportunities from frequency stability and security perspectives. Based on the evaluation of the current situation and future national planning, a number of research gaps and industrial technical issues are identified, and some perspectives on future research directions are presented to help move the power system transformation toward almost 100% renewable and more secure energy systems.</p>

AbstractThe production of Z boson pairs in proton–proton ($${\mathrm{p}} {\mathrm{p}} $$ p p ) collisions, $${{\mathrm{p}} {\mathrm{p}} \rightarrow ({\mathrm{Z}}/\gamma ^*)({\mathrm{Z}}/\gamma ^*) \rightarrow 2\ell 2\ell '}$$ p p → ( Z / γ ∗ ) ( Z / γ ∗ ) → 2 ℓ 2 ℓ ′ , where $${\ell ,\ell ' = {\mathrm{e}}}$$ ℓ , ℓ ′ = e or $${{\upmu }}$$ μ , is studied at a center-of-mass energy of 13$$\,\text {TeV}$$ TeV with the CMS detector at the CERN LHC. The data sample corresponds to an integrated luminosity of 137$$\,\text {fb}^{-1}$$ fb - 1 , collected during 2016–2018. The $${\mathrm{Z}} {\mathrm{Z}} $$ Z Z production cross section, $$\sigma _{\text {tot}} ({\mathrm{p}} {\mathrm{p}} \rightarrow {\mathrm{Z}} {\mathrm{Z}} ) = 17.4 \pm 0.3 \,\text {(stat)} \pm 0.5 \,\text {(syst)} \pm 0.4 \,\text {(theo)} \pm 0.3 \,\text {(lumi)} \text { pb} $$ σ tot ( p p → Z Z ) = 17.4 ± 0.3 (stat) ± 0.5 (syst) ± 0.4 (theo) ± 0.3 (lumi) pb , measured for events with two pairs of opposite-sign, same-flavor leptons produced in the mass region $${60< m_{\ell ^+\ell ^-} < 120\,\text {GeV}}$$ 60 < m ℓ + ℓ - < 120 GeV is consistent with standard model predictions. Differential cross sections are also measured and agree with theoretical predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous $${\mathrm{Z}} {\mathrm{Z}} {\mathrm{Z}} $$ Z Z Z and $${{\mathrm{Z}} {\mathrm{Z}} \gamma }$$ Z Z γ couplings.