Exhaust pollutants from two-stroke petrol engines are a problem for the environment. Biolubricants are a new generation of renewable and eco-friendly vegetable-based lubricants, which have attracted a lot of attention in recent years. In this paper, the applicability of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to support the process of building the scoring system for selecting an appropriate two-stroke lubricant has been analyzed. For this purpose, biolubricants (TMP-triesters) based on castor oil, palm oil, and waste cooking oil were produced and then utilized in a 200 cc two-stroke gasoline engine to investigate their effects on its performance and exhaust emissions. The results obtained from the use of the entropy technique in the TOPSIS algorithm showed that palm oil-based lubricant took up the greatest distance from the Negative Ideal Solution (NIS) and was selected as the most optimal lubricant for these types of engines.

Biolubricants are a new generation of renewable and eco-friendly vegetable-based lubricants which have attracted a lot of attention in recent years. In this research study a back-propagation neural network model has been developed for predicting the effect of different types of biolubricants on the performance and exhaust emissions of a 200 cc two stroke engine. The inputs of the model are lubricant type, lambda and engine speed. Model outputs are: engine brake power, torque, BSFC (brake specific fuel consumption) and exhaust emissions which include CO, CO2, UHC, O2 and NOx emissions. Engine's brake power, torque and brake specific fuel consumption, as well as exhaust emissions have been predicted with the Artificial Neural Network (ANN) model. The relationship between input parameters and engine performance and emissions are determined using the network. The application of ANNs are highly recommended to predict the IC engine parameters under investigation

In recent years, due to the favorable climate conditions of Iran, the cultivation of rapeseed has increased significantly. The aim of this study was to investigate the possibility of biodiesel production from one genotype of rapeseed (TERI (OE) R-983). An ultrasonic approach was used in order to intensify the reaction. Response surface methodology (RSM) was applied to identify the optimum conditions of the process. The results of this research showed that the conversion of biodiesel was found to be 87.175% under the optimized conditions of a 4.63:1 molar ratio (methanol to oil), 56.50% amplitude, and 0.4 s pulses for a reaction time of 5.22 min. Increasing the operating conditions, such as the molar ratio from 4:1 to 5.5:1, amplitude from 50% to 72.5%, reaction time from 3 min to 7 min, and pulse from 0.4 s to 1 s, increased the FAME (fatty acid methyl esters) yield by approximately 4.5%, 2.3%, 1.2%, and 0.5%, respectively. The properties of the TERI (OE) R-983 methyl ester met the requirements of the biodiesel standard (ASTM D6751), indicating the potential of the produced biodiesel as an alternative fuel.