Hot water temperature prediction using a dynamic neural network for absorption chiller application in Indonesia

2018 ◽  
Vol 30 ◽  
pp. 114-120 ◽  
Author(s):  
Nasruddin ◽  
Sholahudin ◽  
M. Idrus Alhamid ◽  
Kiyoshi Saito
Keyword(s):  
Neural Network ◽  
Water Temperature ◽  
Hot Water ◽  
Absorption Chiller ◽  
Temperature Prediction ◽  
Dynamic Neural Network

Comparing various artificial neural network types for water temperature prediction in rivers

2015 ◽  
Vol 529 ◽  
pp. 302-315 ◽  
Author(s):  
Adam P. Piotrowski ◽  
Maciej J. Napiorkowski ◽  
Jaroslaw J. Napiorkowski ◽  
Marzena Osuch
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Water Temperature ◽  
Temperature Prediction ◽  
Artificial Neural

A Dynamic Model of a Single-Stage LiBr-H2O Absorption Chiller

2016 ◽  
Author(s):  
Prangtip Samutr ◽  
Ali Al Alili
Keyword(s):  
Water Temperature ◽  
Dynamic Model ◽  
Cooling Capacity ◽  
Hot Water ◽  
Single Stage ◽  
Coefficient Of Performance ◽  
Maximum Relative Error ◽  
Outlet Temperature ◽  
Absorption Chiller ◽  
Chilled Water

This paper presents a dynamic model of a single-stage LiBr-H2O absorption chiller. A numerical model has been developed based on mass and energy balance equations and heat transfer equations. The model is developed using MATLAB program and the system of non-linear ordinary differential equation is solved using the 4th-order Runge-Kutta method. The model is validated with experimental results from pertained literature. The results show that the maximum relative error is found when comparing the dynamic model predicted chilled water outlet temperature to experimental data, which is around 9%. The effect of the inlet hot water temperature on the hot, cooling and chilled water outlet temperatures, cooling capacity and coefficient of performance (COP) are also studied. The results show that as the hot water outlet temperature increases, the outlet temperatures of cooling and chilled water slightly change. Moreover, the cooling capacity increases and the COP slight decreases as the hot water temperature increases.

State of charge estimation for lithium-ion batteries using dynamic neural network based on sine cosine algorithm

2021 ◽  
pp. 095440702110180
Author(s):  
Meng Wei ◽  
Min Ye ◽  
Jia Bo Li ◽  
Qiao Wang ◽  
Xin Xin Xu
Keyword(s):  
Neural Network ◽  
Lithium Ion Batteries ◽  
Unscented Kalman Filter ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector ◽  
Battery Management System ◽  
Dynamic Neural Network ◽  
Soc Estimation ◽  
Sine Cosine Algorithm

State of charge (SOC) of the lithium-ion batteries is one of the key parameters of the battery management system, which the performance of SOC estimation guarantees energy management efficiency and endurance mileage of electric vehicles. However, accurate SOC estimation is a difficult problem owing to complex chemical reactions and nonlinear battery characteristics. In this paper, the method of the dynamic neural network is used to estimate the SOC of the lithium-ion batteries, which is improved based on the classic close-loop nonlinear auto-regressive models with exogenous input neural network (NARXNN) model, and the open-loop NARXNN model considering expected output is proposed. Since the input delay, feedback delay, and hidden layer of the dynamic neural network are usually selected by empirically, which affects the estimation performance of the dynamic neural network. To cover this weakness, sine cosine algorithm (SCA) is used for global optimal dynamic neural network parameters. Then, the experimental results are verified to obtain the effectiveness and robustness of the proposed method under different conditions. Finally, the dynamic neural network based on SCA is compared with unscented Kalman filter (UKF), back propagation neural network based on particle swarm optimization (BPNN-PSO), least-squares support vector machine (LS-SVM), and Gaussian process regression (GPR), the results show that the proposed dynamic neural network based on SCA is superior to other methods.

scholarly journals Minimum Number of Experimental Data for the Thermal Characterization of a Hot Water Storage Tank

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4741
Author(s):  
María Gasque ◽  
Federico Ibáñez ◽  
Pablo González-Altozano
Keyword(s):  
Experimental Data ◽  
Water Temperature ◽  
Temperature Profile ◽  
Storage Tank ◽  
Water Storage ◽  
Hot Water ◽  
Experimental Temperature ◽  
Inlet Temperature ◽  
Water Storage Tank ◽  
Minimum Number

This paper demonstrates that it is possible to characterize the water temperature profile and its temporal trend in a hot water storage tank during the thermal charge process, using a minimum number of thermocouples (TC), with minor differences compared to experimental data. Four experimental tests (two types of inlet and two water flow rates) were conducted in a 950 L capacity tank. For each experimental test (with 12 TC), four models were developed using a decreasing number of TC (7, 4, 3 and 2, respectively). The results of the estimation of water temperature obtained with each of the four models were compared with those of a fifth model performed with 12 TC. All models were tested for constant inlet temperature. Very acceptable results were achieved (RMSE between 0.2065 °C and 0.8706 °C in models with 3 TC). The models were also useful to estimate the water temperature profile and the evolution of thermocline thickness even with only 3 TC (RMSE between 0.00247 °C and 0.00292 °C). A comparison with a CFD model was carried out to complete the study with very small differences between both approaches when applied to the estimation of the instantaneous temperature profile. The proposed methodology has proven to be very effective in estimating several of the temperature-based indices commonly employed to evaluate thermal stratification in water storage tanks, with only two or three experimental temperature data measurements. It can also be used as a complementary tool to other techniques such as the validation of numerical simulations or in cases where only a few experimental temperature values are available.

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