Solar energy is infinite and environmental-friendly energy that is widely used in many fields such as solar cells, solar roofs, solar dryers and so on. However, solar energy has never been used in anaerobic digestion processes for biogas production. The use of solar energy may accelerate the biogas reaction to achieve higher gas production rates. Hence, the purpose of this research was to study a two-stage anaerobic digestion system in combination with a greenhouse solar dryer to increase the rate of biogas production. The study consisted of two parts: the first was to study the temperature variations in greenhouse solar dryers using computational fluid dynamic techniques, and the second was to conduct two-stage anaerobic digestion integrated with the greenhouse solar dryer system for biogas production. The results showed that the average temperature and biogas production rate in the integrated two-stage anaerobic digestion system compared to a conventional system increased by 16.2% and 19.69 %, respectively

In citronella oil extraction process by steam distillation, inefficient use of steam is the main cause of excessive energy consumption that affects energy cost and oil yield. This research is aimed to reduce the energy cost and increase the oil yield by studying the steam used in the process. The proposed method is the three-stage extraction model combined with the Data Envelopment Analysis developed by Charnes, Cooper and Rhodes (DEA-CCR model). Although the three-stage extraction model has been widely used, there is no research integrate this model with DEA-CCR model. It is well known that DEA-CCR model is an effective tool to evaluate efficiency of decision making units/alternatives. The advantages of this research were presented as the calculation of the optimum distillation conditions, including the steam flow rate and the distillation time, were achieved as discussed in this article. The study was comprised of 3 parts. Firstly, the three-stage extraction model for citronella oil was formulated. Secondly, the results of the proposed model were calculated under different conditions, classified by steam flow rates from 5,000 to 60,000 cm3/min for the distillation period of 15–180 min. Finally, the DEA-CCR model was utilized to evaluate and rank alternatives. The results expressed that the best condition for producing citronella oil was at the steam flow rate of 40,000 cm3/min and the distillation time of 60 min. The optimal energy cost and percentage of oil yield were equal to 0.440 kWh/mL and 0.7%, respectively. When comparing to the experimental results, the percentage error of optimal energy cost and oil yield were slightly different, with a value of 0.98% and 0.85%, respectively. Moreover, the energy consumption was also reduced by 34.6% compared to the traditional operating conditions.

Cross-efficiency measurement in data envelopment analysis (DEA) was developed to overcome the main disadvantage of DEA in discriminating decision making units (DMUs). However, the results obtained from each cross-efficiency model (Benevolent and aggressive models) may not generally be the same for similar problems, and each model may provide different viewpoints that we should take each model into account at the same time. Since Gibbs entropy is one of powerful tools to measure uncertainty, in this paper a novel linear programming model based on the concepts of Gibbs entropy (GE model) has been offered to combine cross-efficiency scores, which are obtained from the viewpoints of benevolent and aggressive models, for ranking DMUs. In order to validate the proposed GE model, it is tested with two examples, including the performance assessment problem and the relative efficiency of seven Thai provinces. The main advantages of the GE model are that it can be used to tackle large size problems with uncertainty, and it can be used to combine other models for ranking DMUs. In addition, the set of multiple solutions of optimal weights for each model can be ignored. By using the proposed model, decision-makers can achieve more reliable decision than individual models.

The Vehicle Routing Problem with Time Windows (VRPTW) is a kind of important variant of VRP with adding time windows constraints to the model. The VRPTW is classified as an NP-hard problem. Hence, the use of exact optimization techniques may be hard to solve these problems in acceptable CPU times, when the problem involves real-world data sets that are very large. To solve this problem, this paper suggests a hybrid genetic algorithm (hybrid GA) combined with Push Forward Insertion Heuristic (PFIH) to make an initial solution instead of traditional GA and three local searches to neighborhood search and improving method. The proposed algorithm was tested on fourteen instances from an online data set in the Solomon`s 56 benchmark problems-selected randomly. The results indicate the good quality of the proposed algorithm.