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Using hybrid renewable energy systems is a smart choice for reducing the carbon emitted by power plants. It also helps climate change mitigation and global warming, leading to universal health for humans and the environment. The efficiency of these systems depends on choosing the right combination of renewable sources, their sizes, and proper scheduling of the generating units. This paper suggests an optimisation method for sizing a geothermal/PV/wind/diesel system, both in off-grid and grid-connected configurations. In the grid-connected mode, the possibility of selling surplus energy generated by renewable resources to the network is considered. Investigations reveal that without accurate control of geothermal reservoirs, they may become depleted. In this study, a strategy is suggested for unit commitment; moreover, the harmony search algorithm is used to find the optimal size of the hybrid system in both configurations. The effectiveness of the proposed approach is represented by simulating an HRES for Ferdows/Iran. [ABSTRACT FROM AUTHOR]

In this paper, the main and interaction effects of chemical reagent (collector and frother) dosages on the copper flotation performance using response surface methodology in design expert software were examined and the optimized dosage of reagents were specified. Then, in order to increase copper grade and recovery at industrial rougher flotation cells of the Miduk copper concentrator, Iran, distribution patterns of chemical reagents in suggested optimum conditions were investigated. A central composite design with three levels and five variables was employed to model and optimize the laboratory batch scale flotation process. The effect of dosages of Z11 (0–10 g/t), Flomin C4132 (14–34 g/t), A3477 (5–15 g/t) as collectors, AF65 (5–15 g/t) and MIBC (4–12 g/t) as frothers was studied. Forty-eight batch rougher flotation experiments (pH 11.5) for Miduk porphyry copper ore consisting of 0.9% Cu were conducted at different conditions using a bench scale Denver flotation cell with 3 L capacity. Second order response functions were generated for both the Cu grade and recovery models, as metallurgical indicators. These model equations were optimized using quadratic programming so as to maximize Cu grade and recovery within the experimental range studied. The main effect of all reagents dosages and the interaction of the Z11-Flomin C4132, Z11-A3477, and A3477-MIBC dosages had a significant impact on both recovery and grade. The optimum reagents dosages were found to be 5 g/t Z11, 34 g/t C4132, 5 g/t A3477, 15 g/t AF65, and 8 g/t MIBC. Maximum recovery of 91.4% and grade 8.13 were achieved under optimal conditions. Additionally, verification experiments at optimum conditions proposed by the models were carried out to specify the validity and adequacy of the predicted models. Based on the industrial rougher circuit tests results, distribution patterns of 75%-0%-25% and 50%-25%-25% were selected as the best distributions of collectors and frothers in mentioned conditions, respectively. The copper recovery and grade improved about 2% and 1.8%, respectively, via the use of optimized stage addition of reagents. [ABSTRACT FROM AUTHOR]

This paper combines optimization (LINGO) and simulation (HEC-ResSim) models to determine monthly operating rules for the Zayandeh Rud Reservoir system in Iran. Based on the optimized flow determined in the single-objective framework, system behaviour was simulated over 47 years. The results show that optimizing the operation of Zayandeh Rud Reservoir could increase its storage by 88.9% and increase the reliability index of regulated water for all downstream demands by over 10%. [ABSTRACT FROM AUTHOR]

This paper applies a novel adaptive approach consisting of Artificial Neural Network (ANN) and Fuzzy Linear Regression (FLR) to improve car ownership forecasting in complex, ambiguous, and uncertain environments. This integrated approach is applied to forecast car ownership in Iran from 1930 to 2007. In this study, the level of car ownership is viewed as the result of demographic, politico-social, and urban structure factors including average family size, total population density, urban population density, urbanization rate, gross national product per capita, gasoline price, and total road length. To capture the potential complexity, uncertainty, and linearity relation between the car ownership function and its determinants, ANN and FLR (including eight well-known FLR) approaches are applied to the collected data. Next, the preferred ANN is selected based on sensitivity analysis results for the test data while the preferred FLR is identified with regard to ANOVA and MAPE results. The results obtained from the performance comparison demonstrate the considerable superiority of the preferred ANN over the preferred FLR regarding the nonlinear and complex nature of the car ownership function in Iran. This is the first study that presents an ANN-FLR approach for car ownership forecasting capable of handling complexity and non-linearity, uncertainty, pre-processing, and post-processing. [ABSTRACT FROM PUBLISHER]