Soft computing-based modeling and emission control/reduction of a diesel engine fueled with carbon nanoparticle-dosed water/diesel emulsion fuel.

This study was set up to model and optimize the performance and emission characteristics of a diesel engine fueled with carbon nanoparticle-dosed water/ diesel emulsion fuel using a combination of soft computing techniques. Adaptive neuro-fuzzy inference system tuned by particle swarm algorithm was used for modeling the performance and emission parameters of the engine, while optimization of the engine operating parameters and the fuel composition was conducted via multiple-objective particle swarm algorithm. The model input variables were: injection timing (35-41° CA BTDC), engine load (0-100%), nanoparticle dosage (0-150 µM), and water content (0-3 wt%). The model output variables included: brake specific fuel consumption, brake thermal efficiency, as well as carbon monoxide, carbon dioxide, nitrogen oxides, and unburned hydrocarbons emission concentrations. The training and testing of the modeling system were performed on the basis of 60 data patterns obtained from the experimental trials. The effects of input variables on the performance and emission characteristics of the engine were thoroughly analyzed and comprehensively discussed as well. According to the experimental results, injection timing and engine load could significantly affect all the investigated performance and emission parameters. Water and nanoparticle addition to diesel could markedly affect some performance and emission parameters. The modeling system could predict the output parameters with an R2 > 0.93, MSE < 5.70 × 10-3, RMSE < 7.55 × 10-2, and MAPE < 3.86 × 10-2. The optimum conditions were: injection timing of 39° CA BTDC, engine load of 74%, nanoparticle dosage of 112 µM, and water content of 2.49 wt%. The carbon dioxide, carbon monoxide, nitrogen oxides, and unburned hydrocarbon emission concentrations were found to be 7.26 vol% , 0.46 vol% , 95.7  ppm, and 36.2 ppm, respectively, under the selected optimal operating conditions while the quantity of brake thermal efficiency was found at an acceptable level ( 34.0 %). In general, the applied soft computing combination appears to be a promising approach to model and optimize operating parameters and fuel composition of diesel engines.

Combined life cycle assessment and artificial intelligence for prediction of output energy and environmental impacts of sugarcane production.

This study aims to employ two artificial intelligence (AI) methods, namely, artificial neural networks (ANNs) and adaptive neuro fuzzy inference system (ANFIS) model, for predicting life cycle environmental impacts and output energy of sugarcane production in planted or ratoon farms. The study is performed in Imam Khomeini Sugarcane Agro-Industrial Company (IKSAIC) in Khuzestan province of Iran. Based on the cradle to grave approach, life cycle assessment (LCA) is employed to evaluate environmental impacts and study environmental impact categories of sugarcane production. Results of this study show that the consumed and output energies of sugarcane production are in average 172,856.14 MJ ha-1, 120,000 MJ ha-1 in planted farms and 122,801.15 MJ ha-1, 98,850 MJ ha-1 in ratoon farms, respectively. Results show that, in sugarcane production, electricity, machinery, biocides and sugarcane stem cuttings have the largest impact on the indices in planted farms. However, in ratoon farms, electricity, machinery, biocides and nitrogen fertilizers have the largest share in increasing the indices. ANN model with 9-10-5-11 and 7-9-6-11 structures are the best topologies for predicting environmental impacts and output energy of sugarcane production in planted and ratoon farms, respectively. Results from ANN models indicated that the coefficient of determination (R2) varies from 0.923 to 0.986 in planted farms and 0.942 to 0.982 in ratoon farms in training stage for environmental impacts and outpt energy. Results from ANFIS model, which is developed based on a hybrid learning algorithm, showed that, for prediction of environmental impacts, R2 varies from 0.912 to 0.978 and 0.986 to 0.999 in plant and ratoon farms, respectively, and for prediction of output energy, R2 varies from 0.944 and 0.996 in planted and ratoon farms. Results indicate that ANFIS model is a useful tool for prediction of environmental impacts and output energy of sugarcane production in planted and ratoon farms.

Modeling of yield and environmental impact categories in tea processing units based on artificial neural networks.

In this study, an artificial neural network (ANN) model was developed for predicting the yield and life cycle environmental impacts based on energy inputs required in processing of black tea, green tea, and oolong tea in Guilan province of Iran. A life cycle assessment (LCA) approach was used to investigate the environmental impact categories of processed tea based on the cradle to gate approach, i.e., from production of input materials using raw materials to the gate of tea processing units, i.e., packaged tea. Thus, all the tea processing operations such as withering, rolling, fermentation, drying, and packaging were considered in the analysis. The initial data were obtained from tea processing units while the required data about the background system was extracted from the EcoInvent 2.2 database. LCA results indicated that diesel fuel and corrugated paper box used in drying and packaging operations, respectively, were the main hotspots. Black tea processing unit caused the highest pollution among the three processing units. Three feed-forward back-propagation ANN models based on Levenberg-Marquardt training algorithm with two hidden layers accompanied by sigmoid activation functions and a linear transfer function in output layer, were applied for three types of processed tea. The neural networks were developed based on energy equivalents of eight different input parameters (energy equivalents of fresh tea leaves, human labor, diesel fuel, electricity, adhesive, carton, corrugated paper box, and transportation) and 11 output parameters (yield, global warming, abiotic depletion, acidification, eutrophication, ozone layer depletion, human toxicity, freshwater aquatic ecotoxicity, marine aquatic ecotoxicity, terrestrial ecotoxicity, and photochemical oxidation). The results showed that the developed ANN models with R 2 values in the range of 0.878 to 0.990 had excellent performance in predicting all the output variables based on inputs. Energy consumption for processing of green tea, oolong tea, and black tea were calculated as 58,182, 60,947, and 66,301 MJ per ton of dry tea, respectively.