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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.

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.

The study evaluates the potential of different vegetable wastes namely, composite vegetable waste (CVW), potato waste (PW), sweet potato waste (SPW) and yam waste (YW) as an alternative feedstock for the production of renewable sugars. Thermal assisted chemical pretreatment followed by enzymatic saccharification yielded maximum sugars (0.515 g/g CVW, 0.56 g/g PW, 0.57 g/g SPW and 0.56 g/g YW) with total carbohydrate depolymerization of 95.01%, 88.30%, 90.32% and 88.59% respectively. Obtained sugars were valorized into bioethanol through fermentation using S. cerevisiae by optimizing the pH and temperature. The highest ethanol yield of 251.85 mg/g was obtained from SPW at 35°C followed by YW (240.98 mg/g), PW (235.4 mg/g) and CVW (125.6 mg/g) at pH 5.0. Utilizing the abundantly available vegetable wastes as a renewable feedstock for reducing sugars and subsequent bioethanol production will influence the economics and sustainability of the process positively in circular biorefinery format.

The development of photobioreactor is important for sustainable production of renewable fuels, wastewater treatment and CO2 fixation. For the design and scale-up of a photobioreactor, CFD can be used as an indispensable tool. The present study reviews the recent status of computational flow modelling of various types of photobioreactors, involving fluid dynamics, light transport, and algal growth kinetics. An integrated modelling approach of hydrodynamics, light intensity, mass transfer, and biokinetics in photobioreactor is discussed further. Also, this reviews intensified system to improve the mixing, and light intensity of photobioreactors. Finally, the prospects and challenges of CFD modelling in photobioreactors are discussed. Multi-scale modelling approach and development of low-cost efficient computational framework are the areas to be considered for modelling of photobioreactor in near future. In addition, it is necessary to use process intensification techniques for photobioreactors for improving their hydrodynamics, mixing and mass transfer performances, and algal growth productivity.

Abstract Office buildings consume a significant amount of energy that can be reduced through behavioral change. Gamification offers the means to influence the energy consumption related to the activities of the office users. This paper presents a new mobile gamification platform to foster the adoption of energy efficient behaviors in office buildings. The gamification platform is a mobile application with multiple types of dashboards, such as (1) an information dashboard to increase the awareness of the users about their energy consumption and footprint, (2) a gaming dashboard to engage users in real-time energy efficiency competitions, (3) a leaderboard to promote peer competition and comparison, and (4) a message dashboard to send tailor-made messages about energy efficiency opportunities. The engagement and gamification strategies embedded in these dashboards exploit economic, environmental, and social motivations to stimulate office users to adopt energy efficient behaviors without compromising their comfort and autonomy levels. The gamification platform was demonstrated in an office building environment. The results suggest electricity savings of 20%.