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Abstract To achieve the EU climate and energy objectives, a transition towards a future sustainable energy system is needed. The integration of the huge potential for industrial waste heat recovery into smart energy system represents a main opportunity to accomplish these goals. To successfully implement this strategy, all the several stakeholders' conflicting objectives should be considered. In this paper an evolutionary multi-objective optimization model is developed to perform a sustainability evaluation of an energy system involving an industrial facility as the waste heat source and the neighbourhood as district heating network end users. An Italian case study of heat recovery from a steel casting facility shows how the model allows to properly select the district heating network set of users to fully exploit the available waste energy. Design directions such as the thermal energy storage capacity can be also provided. Moreover, the model enables the analysis of the trade-off between the stakeholders’ different perspectives, allowing to identify possible win-win solutions for both the industrial sector and the citizenship.

Abstract In this study, crude cellulose derived from cornstalk, after bleaching, was used as raw material for the synthesis of sodium carboxymethyl cellulose (CMC) by reacting with the cellulose with NaOH and chloroacetic acid at 75 °C for 1.5 h. Effects of alkali dosage, concentration of chloroacetic acid on the physical and chemical properties of the CMC products were investigated. It was revealed that the reactants alkali reagent/chloroacetic acid/cellulose at the molar ratio of 4.6:2.8:1and 4:2.5:1, or at the molar ratio of NaOH/ClCH 2 COOH ≈1.6–1.64, resulted in CMC products of relatively high water solubility. The viscosity-average molecular weight M v of these two CMC products obtained at molar ratios of 4.0:2.5:1 and 4.6:2.8:1 is in the range of 1.94 × 10 4 –2.48 × 10 4 g mol −1 , and the average DS of the two products are 0.57 and 0.85, respectively. As the solute concentration is above 2 wt%, the viscosity of the CMC-water solution exhibits nonlinear (exponential) increasing with increasing the solute concentration (typical of non-Newton fluids).

The electrical resistance of Ni 50 Ti 30 Hf 20 and Ni 50 Ti 40.5 Hf 9.5 alloys during Stress Assisted Two Way Memory Effect was investigated with the aim to deduce the relationship between electrical resistance and deformation across the transformation range. Results show an almost linear relationship although a crossing point between increasing and decreasing deformation may compromise the use of these alloys actuators.

A detailed analysis of the effects of ground grid configuration on grounding performance has been carried out for uniform and horizontally stratified soils with multiple layers. The results of the analysis reveal that the most efficient and cost-effective design is highly dependent on soil structure type and characteristics. In the absence of ground rods, grounding grids with uniform mesh size are quite efficient in soils having a thin (relative to grid size) high resistivity (relative to the lower layers) top soil, while grids with small mesh size at the periphery of the grid provide optimum performance in uniform soils and soils with low resistivity top soils. Ground rods were found to be effective only when a significant portion of their length is in contact with a low resistivity soil, as expected.

Short-term generation scheduling is an important function in daily operational planning of power systems. It is defined as optimal scheduling of power generators over a scheduling period while respecting various generator constraints and system constraints. Objective of the problem includes costs associated with energy production, start-up cost and shut-down cost along with profits. The resulting problem is a large scale nonlinear mixed-integer optimization problem for which there is no exact solution technique available. The solution to the problem can be obtained only by complete enumeration, often at the cost of a prohibitively computation time requirement for realistic power systems. This paper presents a hybrid algorithm which combines Lagrangian Relaxation (LR) together with Evolutionary Algorithm (EA) to solve the problem in cooperative and competitive energy environments. Simulation studies were carried out on different systems containing various numbers of units. The outcomes from different algorithms are compared with that from the proposed hybrid algorithm and the advantages of the proposed algorithm are briefly discussed.

Abstract Bio-based polyurethane (BPU) foams were successfully prepared using hydrothermally liquefied wheat straw (WS) to substitute a mass fraction of up to 50% of polyols. Response surface methodology (RSM) based on central composite design (CCD) was employed to optimize four process parameters: NCO/OH molar ratio, loading of crosslinking agent (glycerol), loading of catalyst (a mixture of triethylene diamine, stannous octoate, and triethanolamine), and loading of blowing agent (water) for the maximum compression strength of the rigid BPU foams. With the quadratic orthogonal regression model, verified by experimentation, the maximum compression strength of approximately 180 kPa was obtained at the following optimal conditions: NCO/OH molar ratio of 1.24:1, glycerol addition of 12.11%, catalyst loading of 0.76%, and blowing agent addition of 1.31% in relation to the total mass of polyols. The BPU foam prepared at the optimal conditions exhibits good thermal conductivity (0.045 Wm−1K−1) and thermal stability, comparable to those of a reference foam prepared with 100% PPG400.

Microgrids are increasingly being used as a platform to integrate distributed generation such as renewable energy sources and (RESs) conventional sources in both grid-connected and isolated power systems. Due to the inherent intermittent nature of RESs, energy storage systems (ESSs) that can absorb fluctuations have become inevitable. Nevertheless, large capacities of ESSs increase the initial cost while small capacities lead to instabilities and increase in the cost of conventional fuels. Therefore, finding the optimal size of the ESS for a given application is essential for the reliable, efficient and economical operation of a microgrid. Once the battery size is decided, maintaining its energy at appropriate levels is essential to ensure stable and safe operation of the microgrid. This paper presents a novel expert fuzzy system - grey wolf optimization (FL-GWO) based intelligent meta-heuristic method for battery sizing and energy management. The proposed energy management operation is carried out by a Grey Wolf Optimiser (GWO) that is helped to set the membership functions and rules of the fuzzy logic expert system. The unit commitment (UC) issue, which is essential for the proper operation of the isolated microgrid, has been additionally considered in this paper. To verify the performance of the proposed method, results are compared with the rules-based method and traditional GWO algorithm. It has been proven from the results that the FL-GWO has a significant convergence property and capability to minimize the Levelized Cost Of Electricity (LCOE) by 14.13% and 24.15% compared with conventional GWO algorithm and rules-based method, respectively. The weather conditions for different climates is used to verify the performance of the intelligent energy management method under different operating scenarios. The results show that the intelligent online multi-objective energy management strategy is capable of managing a smooth power flow with the same optimal configuration in the isolated microgrid, minimising the fossil fuel utilisation and reducing the CO2 emission level.