scholarly journals Prediction of Waste Heat Energy Recovery Performance in a Naturally Aspirated Engine Using Artificial Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Abdul Hakim Rohhaizan ◽  
Azma Putra ◽  
Ahmad Faris Ismail
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Waste Heat ◽  
Simulation Method ◽  
Spark Ignition Engine ◽  
Test Results ◽  
Simulation Test ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Heat Energy Recovery

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM). The experimental and simulation test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

scholarly journals Prediction on Power Produced from Power Turbine as a Waste Heat Recovery Mechanism on Naturally Aspirated Spark Ignition Engine Using Artificial Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Abdul Hakim Rohhaizan ◽  
Ahmad Faris Ismail ◽  
Shamsul Anuar Shamsudin ◽  
Azma Putra ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Simulation Method ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Recovery Mechanism ◽  
Artificial Neural

The waste heat from exhaust gases represents a significant amount of thermal energy, which has conventionally been used for combined heating and power applications. This paper explores the performance of a naturally aspirated spark ignition engine equipped with waste heat recovery mechanism (WHRM) in a sedan car. The amount of heat energy from exhaust is presented and the experimental test results suggest that the concept is thermodynamically feasible and could significantly enhance the system performance depending on the load applied to the engine. However, the existence of WHRM affects the performance of engine by slightly reducing the power. The simulation method is created using an artificial neural network (ANN) which predicts the power produced from the WHRM.

scholarly journals Prediction of the Dynamic Stiffness of Resilient Materials using Artificial Neural Network (ANN) Technique

2019 ◽  
Vol 9 (6) ◽  
pp. 1088 ◽  
Author(s):  
Changhyuk Kim ◽  
Jung-Yoon Lee ◽  
Moonhyun Kim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Residential Buildings ◽  
Dynamic Stiffness ◽  
Test Results ◽  
High Rise ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Sound Reduction ◽  
Reduction Capacity

High-rise residential buildings are constructed in countries with high population density in response to the need to utilize small development areas. As many high-rise buildings are being constructed, issues of floor impact sound tend to occur in buildings. In general, resilient materials are implemented between the slab and the finishing mortar to control the floor impact sound. Various mechanical properties of resilient materials can affect the floor impact sound. To investigate the impact sound reduction capacity, various experimental tests were conducted. The test results show that the floor impact sound reduction capacity has a close relationship with the dynamic stiffness of resilient materials. A total of six different kinds of resilient materials were loaded under four loading conditions. The test results show that loading time, loading, and material properties influence the change in dynamic stiffness. Artificial neural network (ANN) technique was implemented to obtain the responses between the deflection and dynamic stiffness. Three different algorithms were considered in the ANN models and the trained results were analyzed based on the root mean square error. The feasibility of using the ANN technique was verified with a high and consistent level of accuracy.

Prediction of the Density & Thermal Conductivity of Light Bricks on the Effect of Aluminium Elements Using Artificial Neural Network (ANN)

2019 ◽  
Vol 964 ◽  
pp. 270-279
Author(s):  
Zulkifli ◽  
Gede Panji
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Raw Materials ◽  
Simulation Method ◽  
Acoustic Properties ◽  
Main Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Element Variation

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.

scholarly journals Prediction of generated power from steam turbine waste heat recovery mechanism system on naturally aspirated spark ignition engine using artificial neural network

2017 ◽  
Vol 22 (18) ◽  
pp. 5955-5964 ◽  
Author(s):  
Safarudin Gazali Herawan ◽  
Kamarulhelmy Talib ◽  
Azma Putra ◽  
Ahmad Faris Ismail ◽  
Shamsul Anuar Shamsudin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Steam Turbine ◽  
Heat Recovery ◽  
Waste Heat Recovery ◽  
Waste Heat ◽  
Spark Ignition ◽  
Spark Ignition Engine ◽  
Recovery Mechanism ◽  
Artificial Neural

scholarly journals Nonlinear analysis and prediction of soybean futures

2021 ◽  
Vol 67 (No. 5) ◽  
pp. 200-207
Author(s):  
Tao Yin ◽  
Yiming Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Local Approximation ◽  
Gaussian Kernel ◽  
Test Results ◽  
Agricultural Futures ◽  
Existence Test ◽  
Benchmark System ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

We use chaotic artificial neural network (CANN) technology to predict the price of the most widely traded agricultural futures – soybean futures. The nonlinear existence test results show that the time series of soybean futures have multifractal dynamics, long-range dependence, self similarity, and chaos characteristics. This also provides a basis for the construction of a CANN model. Compared with the artificial neural network (ANN) structure as our benchmark system, the predictability of CANN is much higher. The ANN is based on Gaussian kernel function and is only suitable for local approximation of nonstationary signals, so it cannot approach the global nonlinear chaotical hidden pattern. Improving the prediction accuracy of soybean futures prices is of great significance for investors, soybean producers, and decision makers.

Application of the Artificial Neural Network (ANN) Method as MPPT Photovoltaic for DC Source Storage

2019 ◽  
Vol 12 (3) ◽  
pp. 145 ◽  
Author(s):  
Epyk Sunarno ◽  
Ramadhan Bilal Assidiq ◽  
Syechu Dwitya Nugraha ◽  
Indhana Sudiharto ◽  
Ony Asrarul Qudsi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann
Sign in / Sign up

Export Citation Format

Share Document