Experimental Investigation of Organic Rankine Cycle (ORC) for Low Temperature Geothermal Fluid: Effect of Pump Rotation and R-134 Working Fluid in Scroll-Expander

Organic Rankine Cycle(ORC)is one of the solutions to utilize a low temperature geothermal fluid for power generation.The ORC system can be placed at the exit of the separator to extract energy from brine.Furthermore,one of the main components of the system and very important is the pump.Therefore,in this research,the pump rotation is examined to investigate the effect on power output and energy efficiency for low temperature geothermal fluid.The rotation is determined by using an inverter with the following frequencies:7.5,10,12.5,15 and 17.5 Hz,respectively.R-134 working fluid is employed with 373.15 K evaporator temperature in relation to the low temperature of the geothermal fluid.Furthermore,the condenser temperature and fluid pressure were set up to 293.15 K and 5×10^(5) Pa,respectively.This research uses a DC generator with a maximum power of 750 Watt and the piping system is made from copper alloy C12200 ASTM B280 with size 1.905×10^(−2) m and a thickness of 8×10^(−4) m.The results showed that there is an increase in mass flow rate,enthalpy and generator power output along with increasing pump rotation.In addition,it showed that the maximum generator output power was 377.31 Watt at the highest pump rotation with a frequency of 17.5 Hz.

Upgrading the Quality of Solid Fuel Made from Nyamplung (Calophyllum inophyllum) Wastes Using Hydrothermal Carbonization Treatment

One of the major problems faced in managing biomass waste to higher quality products is choosing the right technology.Wastes are used as an alternative fuel,with increase in the calorific value.Hydrothermal carbonization(HTC)is a biomass conversion technology,used to obtain solid fuel.This study aims to utilize of Calophyllum inophyllum as an alternative solid fuel through HTC.The calorific value and proximate of the hydrochar will be determined and analyzed to find out its quality.The experiments were carried out at temperature variations of 160℃,190℃,and 220℃ and holding times of 30 and 60 minutes.The results show that an increase in temperature and holding time causes a decline in the moisture content 1.87%,volatile matter 54.03%,and ash content 12.35%,respectively,leading to elevations in the fixed carbon at 31.75%.In addition,the highest calorific value of 4149 Kcal/Kg was produced at a temperature of 220℃,within a holding time of 60 minutes.The results showed a significant increase in the quality of solid fuels between 3500–4611 Kcal/Kg in accordance with the American Standard Testing and Materials(ASTM).Therefore,this research leads to an important finding that Calophyllum inophyllum waste through the HTC process can be used as an alternative fuel to substitute lignite coal,which is environmentally friendly.

Long-Term Electricity Demand Forecasting for Malaysia Using Artificial Neural Networks in the Presence of Input and Model Uncertainties

Electricity demand is also known as load in electric power system.This article presents a Long-Term Load Forecasting(LTLF)approach for Malaysia.An Artificial Neural Network(ANN)of 5-layer Multi-Layered Perceptron(MLP)structure has been designed and tested for this purpose.Uncertainties of input variables and ANN model were introduced to obtain the prediction for years 2022 to 2030.Pearson correlation was used to examine the input variables for model construction.The analysis indicates that Primary Energy Supply(PES),population,Gross Domestic Product(GDP)and temperature are strongly correlated.The forecast results by the proposed method(henceforth referred to as UQ-SNN)were compared with the results obtained by a conventional Seasonal Auto-Regressive Integrated Moving Average(SARIMA)model.The R^(2)scores for UQ-SNN and SARIMA are 0.9994 and 0.9787,respectively,indicating that UQ-SNN is more accurate in capturing the non-linearity and the underlying relationships between the input and output variables.The proposed method can be easily extended to include other input variables to increase the model complexity and is suitable for LTLF.With the available input data,UQ-SNN predicts Malaysia will consume 207.22 TWh of electricity,with standard deviation(SD)of 6.10 TWh by 2030.

Reliability Based Multi-Objective Thermodynamic Cycle Optimisation of Turbofan Engines Using Luus-Jaakola Algorithm

Aircraft engine design is a complicated process,as it involves huge number of components.The design process begins with parametric cycle analysis.It is crucial to determine the optimum values of the cycle parameters that would give a robust design in the early phase of engine development,to shorten the design cycle for cost saving and man-hour reduction.To obtain a robust solution,optimisation program is often being executed more than once,especially in Reliability Based Design Optimisations(RBDO)with Monte-Carlo Simulation(MCS)scheme for complex systems which require thousands to millions of optimisation loops to be executed.This paper presents a fast heuristic technique to optimise the thermodynamic cycle of two-spool separated flow turbofan engines based on energy and probability of failure criteria based on Luus-Jaakola algorithm(LJ).A computer program called Turbo Jet Engine Optimiser v2.0(TJEO-2.0)has been developed to perform the optimisation calculation.The program is made up of inner and outer loops,where LJ is used in the outer loop to determine the design variables while parametric cycle analysis of the engine is done in the inner loop to determine the engine performance.Latin-Hypercube-Sampling(LHS)technique is used to sample the design and model variations for uncertainty analysis.The results show that optimisation without reliability criteria may lead to high probability of failure of more than 11%on average.The thrust obtained with uncertainty quantification was about 25%higher than the one without uncertainty quantification,at the expense of less than 3%of fuel consumption.The proposed algorithm can solve the turbofan RBDO problem within 3 min.

A Novel Power Curve Prediction Method for Horizontal-Axis Wind Turbines Using Artificial Neural Networks

Accurate prediction of wind turbine power curve is essential for wind farm planning as it influences the expected power production.Existing methods require detailed wind turbine geometry for performance evaluation,which most of the time unattainable and impractical in early stage of wind farm planning.While significant amount of work has been done on fitting of wind turbine power curve using parametric and non-parametric models,little to no attention has been paid for power curve modelling that relates the wind turbine design information.This paper presents a novel method that employs artificial neural network to learn the underlying relationships between 6 turbine design parameters and its power curve.A total of 198 existing pitch-controlled and active stall-controlled horizontal-axis wind turbines have been used for model training and validation.The results showed that the method is reliable and reasonably accurate,with average R^(2)score of 0.9966.