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Production of biodiesel from yellow grease (waste cooking oil and waste animal fats) is fast emerging as a promising alternative to address the twin challenges before the biodiesel industry today-fluctuation in prices of vegetable oil and the food versus fuel debate. Yellow grease has a high percentage of free fatty acids (FFA) and proves to be an unsuitable feedstock for biodiesel production from commercially viable alkali-catalyzed production systems due to saponification problems.“Green” methodologies based on heterogeneous solid acid catalyzed reactions have the potential to simultaneously promote esterification and transesterification reactions of yellow grease to produce biodiesel without soap formation and offer easy catalyst separation without generation of toxic streams. This paper presents kinetic studies for the conversion of model yellow grease feeds to biodiesel using a heteropolyacid supported on alumina (HSiW/Al2O3) using a batch autoclave. Three model yellow grease feeds were prepared using canola oil with added FFA such as palmitic, oleic and linoleic acid. A pseudo homogeneous kinetic model for the parallel esterification and transesterification was developed. The rate constants and activation parameters for esterification and transesterification reactions for the model yellow grease feeds were determined. The rate constants for esterification are higher than the transesterification rate constants. The kinetic model was validated using the experimental biodiesel data obtained from processing a commercial yellow grease feed. The kinetic model could be used to design novel processes to convert various low-value waste oils, fats and non-food grade oils to sustainable biodiesel. Keywords: Yellow grease, Canola oil, Free fatty acids, Heteropolyacid, Kinetics for esterification and transesterification

This paper presents a novel modified interactive honey bee mating optimization (IHBMO) base fuzzy stochastic long-term approach for determining optimum location and size of distributed energy resources (DERs). The Monte Carlo simulation method is used to model the uncertainties associated with long-term load forecasting. A proper combination of several objectives is considered in the objective function. Reduction of loss and power purchased from the electricity market, loss reduction in peak load level and reduction in voltage deviation are considered simultaneously as the objective functions. First, these objectives are fuzzified and designed to be comparable with each other. Then, they are introduced into an IHBMO algorithm in order to obtain the solution which maximizes the value of integrated objective function. The output power of DERs is scheduled for each load level. An enhanced economic model is also proposed to justify investment on DER. An IEEE 30-bus radial distribution test system is used to illustrate the effectiveness of the proposed method.

Abstract In-core nuclear fuel management is one of the most important concerns in the design of nuclear reactors. Two main goals in core fuel loading pattern design optimization are maximizing the core effective multiplication factor in order to extract the maximum energy, and keeping the local power peaking factor lower than a predetermined value to maintain the fuel integrity. Because of the numerous possible patterns of fuel assemblies in the reactor core, finding the best configuration is so important and challenging. Different techniques for optimization of fuel loading pattern in the reactor core have been introduced by now. In this study, a software is programmed in C# language to find an order of the fuel loading pattern of a VVER-1000 reactor core using the perturbation theory. Our optimization method is based on minimizing the radial power peaking factor. The optimization process launches by considering an initial loading pattern and the specifications of the fuel assemblies which are given as the input of the software. The results on a typical VVER-1000 reactor reveal that the method could reach to a pattern with an allowed radial power peaking factor and increases the cycle length 1.1 days, as well.

Abstract The power system needs to satisfy its customers demand with minimum cost and emission. Fuel cost has straight relationship with energy cost. This paper presents an advanced parallelized particle swarm optimization algorithm (PSPSO) for finding optimal combination of power generation units that minimizes the total fuel cost and emission. In this algorithm, time requirements for solving complex large-scale CEED problem can be substantially reduced using parallel computation and it performs the update of positions and velocities in the end of each iteration. The proposed approach is applied on four test systems and compared with other techniques. The results represent that PSPSO has better convergence than other techniques.

Abstract Commercial potential in developing countries has always received a great attention from international investors, but this is not the case in Waste-to-Energy sector. Waste-to-Energy is bound up with various uncertainties rooted in its long-term nature therefore incorporating risks regarding political matters in developing countries makes it more complex. The present study substantiates the incompatibility of classic valuation methods in risky projects. Consequently, to deal with the riskiness of Waste-to-Energy investment in less developed countries, the combination of binomial tree analysis and Decoupled NPV is proposed. The hybrid approach is deployed to value a Waste-to-Energy project in Iran, and all evidence attest to the robustness of the method. The contribution of this paper can open up new vistas for investing in Waste-to-Energy industry, thus abating the catastrophic effects of landfill gas emissions.

Abstract Economically harvesting energy from a microbial fuel cell (MFC), increasing its electrical power production, and developing its role as a practical energy supply, needs a low-cost and high-performance design of the MFC compartments. According to this strategy, a novel monolithic membrane electrode assembly (MEA) was fabricated and evaluated as an air–cathode in a single-chamber MFC (SCMFC). The MEA was made of bacterial cellulose (BC), conductive multi-walled carbon nanotubes (CNT), and nano-zycosil (NZ). BC, as a nano-celluloses with oxygen barrier property, can maintain anaerobic conditions for the anode compartment. Binder-less CNT coating on BC avoids costly binders such as poly-tetra fluoro ethylene (PTFE) and Nafion and decreases the MEA charge transfer resistance. NZ, as a very cheap modifier, not only prevents the anolyte leakage but also provides more MEA’s active sites for the oxygen reduction reaction (ORR). The electrochemical performance of the MEA was compared to a PTFE- based gas diffusion electrode (GDE) in the SCMFC. The MEA cell provided a pulse power density of 1790 mW/m2, roughly twice as high as the pulse power density of GDE (920 mW/m2). SCMFC’s internal resistance decreased from 1.84 KΩ (with GDE) to 0.8 KΩ (with MEA). Also, the cell’s columbic efficiency increased from 4.2% (with GDE) to11.7% (with MEA). Additionally, the capacitance of the MEA (65 mF) was much higher than the value for GDE (0.73 mF). Thus, the MEA compared to the GDE showed higher performance in the SCMFC for electricity generation and wastewater treatment at a lower cost.

This paper presents an experimental study of indoor thermal environment near a full-scale glass facade with different types of shading devices under varying climatic conditions in winter. Interior glazing and shading temperature, operative temperature and radiant temperature asymmetry were measured for facade sections with roller shades and venetian blinds at different positions. Interior glass surface temperatures can be high during sunny days with low outdoor temperature. Shading systems significantly improved operative temperature and radiant temperature asymmetry during cold sunny days, depending on their properties and tilt angle. During cloudy days the impact was smaller, however the shading layers could still decrease the amount of heat loss through the facade. A transient building thermal model, which also calculates indoor environmental indices under the presence of solar radiation, was developed and compared with the experimental measurements. Part II of this paper uses this validated model with a transient, two-node thermal comfort model (including transmitted solar radiation) for assessment of indoor environmental conditions with different building envelope and shading properties, facade location and orientation.

A vehicle model is used to evaluate a novel powertrain that is comprised of a dual energy storage system (Dual ESS). The system includes two battery packs with different chemistries and the necessary electronic controls to facilitate their coordination and optimization. Here, a lithium-ion battery pack is used as the primary pack and a Zinc-air battery as the secondary or range-extending pack. Zinc-air batteries are usually considered unsuitable for use in vehicles due to their poor cycle life, but the model demonstrates the feasibility of this technology with an appropriate control strategy, with limited cycling of the range extender pack. The battery pack sizes and the battery control strategy are configured to optimize range, cost and longevity. In simulation the vehicle performance compares favourably to a similar vehicle with a single energy storage system (Single ESS) powertrain, travelling up to 75 km further under test conditions. The simulation demonstrates that the Zinc-air battery pack need only cycle 100 times to enjoy a ten-year lifespan. The Zinc-air battery model is based on leading Zinc-air battery research from literature, with some assumptions regarding achievable improvements. Having such a model clarifies the performance requirements of Zinc-air cells and improves the research community's ability to set performance targets for Zinc-air cells.

Due to their unique nature, construction projects are considered one of the world’s most hazardous and incident-prone industrial sectors. The present study aimed to analyze health, safety and environmental (HSE) risks relating to construction projects based on the project management body of knowledge (PMBOK) and sustainability approach. This study was conducted with the participation of 30 experts, using the semi-quantitative risk assessment technique, in nine areas of the project management’s body of knowledge, based on the fuzzy analytic hierarchy process. Risk, in this study, was estimated using a two-dimensional matrix of incident probability and severity, each of which has four sub-parameters. The HSE risks pertaining to each of the nine areas of PMBOK were identified. After that, the two dimensions of risk, including incident probability and severity, were measured. Thirty-seven risk sources associated with nine areas of the PMBOK were identified. Risk analysis revealed that 20 sources were at an unacceptable risk level, and 17 risks were at a tolerable risk level. Identifying HSE-related risk sources in accordance with the nine areas of PMBOK, and using FAHP to assess the risk of these hazards in construction projects, can lead to a more realistic estimate of risk in construction projects. The presented method in the current study can create a novel perspective in terms of the construction industry’s risk management and assessment.