A sustainable solution for electricity generation using thermo-acoustic technology

Abstract: This work explores the use of thermo-acoustic system as alternative technology for electricity generation. This technology is proposed as a potential replacement for low-cost electrical power generation because of its simplicity and lack of moving parts. Thermo-acoustic generators providing clean electrical energy to power small appliances. The energy conversion from heat into sound wave is done within thermo-acoustic engine. The latter is coupled to a linear alternator for electricity generation. The study investigates the influence of the geometrical configuration of the device on to the whole functionality of the generator. The paper studies the technology through experimental trails performed using a simple arrangement to simulate the generator. The experiment is conducted in phases; the first phase identifies the best geometrical configuration of the thermo-acoustic engine by measuring the sound pressure level and the temperatures. The second phase consist of measuring the electricity generated using a Loudspeaker. The results obtained show the potential for this sustainable solution for electricity generation

Evaluation of the Stirling heat engine performance prediction using ANN-PSO and ANFIS models

Abstract: The work presents the prediction performance results of three algorithms, namely Artificial Neural Network (ANN), Artificial Neural Network trained with Particle Swarm Optimization (PSO) and Adaptive Neuro-Fuzzy Inference System (ANFIS) models. ANFIS and ANN trained by PSO are applied to predict the power and torque values of a Stirling heat engine with a level controlled displacer driving mechanism. Data from experimental work done by Karabulut et al. is used to train and assess the algorithms. MATLAB is used to develop, implement and train the algorithms. The Root Mean Square Error (RMSE, Coefficient of determination (R2) and computational time are used to assess the performance of the algorithms

Sustainable supplier selection in a paint manufacturing company using hybrid meta-heuristic algorithm

Abstract: Supplier selection in a manufacturing system is highly complex due to the stochastic nature and structure of organizations, thereby necessitating a paradigm shift from the rule of thumb and classical methods of supplier selection to a reliable technique, using the hybrid algorithm to provide higher accuracy in the selection process. Hence, this study proposes the use of hybrid computational intelligence technique, Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for effective prediction and sustainable selection of suppliers (SSS). This hybrid modelling configuration was applied in a paint manufacturing company to select the best possible supplier. Information obtained from the company within the period of investigation was fed into the model. The result obtained shows a faster and reliable prediction of the creative model. Professionals and business managers will benefit greatly from SSS in an in-bound and out-bound supply chain system