ANN and ANFIS models for performance evaluation of a vertical ground source heat pump system

Case study of performance evaluation of ground source heat pump system based on ANN and ANFIS models

Techno-economic and spatial analysis of vertical ground source heat pump systems in Germany

Predictive ANN models of ground heat exchanger for the control of hybrid ground source heat pump systems

Performance evaluation under the actual operating condition of a vertical ground source heat pump system in a school building

In this paper, experimental studies were performed for a solar ground source heat pump system (SGSHPS) with a vertical ground heat exchanger (VGHE). The experiment was operated during the summer in 2018. The heat from the solar collector was monitored by measuring the inlet and outlet temperatures and flow rate of the heat transfer fluids. An energy equilibrium balance carried out indicates heat extraction from the solar collector to the ground heat exchanger. It has been established that clear impact is achieved within a radius of 5 m. The average temperature of the actively regenerated borehole was higher than that of the undisturbed profile, which has a direct impact on the significant benefits of the coefficient of performance (COP) of the ground source heat pump system (GSHPS) and effectively helps soil regeneration. The average efficiency ratio of the heat transferred from solar radiation to soil in the SGSHPS was 42.3%.

Evaluating the influence of thermal dispersion on temperature plumes from geothermal systems using analytical solutions

Risk based lifetime costs assessment of a ground source heat pump (GSHP) system design: Methodology and case study

Optimization of ground heat exchangers for space heating and cooling applications using Taguchi method and utility concept

Evaluation of different heat extraction strategies for shallow vertical ground-source heat pump systems

Artificial intelligence-based estimation of flushing half-cone geometry

A ground-source heat pump (GSHP) system is a renewable energy technology that effectively reduces greenhouse gas emissions and consequently mitigates the progression of global warming. The thermal efficiency of ground heat exchangers (GHEs) is a critical component in the GSHP system that must be accurately estimated for its long-term operationality. Therefore, in this study, the thermal performance of double-spiral GHEs incorporated within the thermal piles of a zero-energy building in Sapporo, Japan, was investigated using the actual measured data obtained from the site and a novel metric, namely, the coefficient of heat extraction/injection, for a more precise evaluation. Moreover, this study assessed the coefficient of performance (COP) of the GSHP units during various periods of cooling or heating. The temperature of the circulating fluid remained within an ideal operational range over an operational period of 2 years, and the COP calculations indicated a high operational efficiency. The results derived in this study substantially exceeded those of traditional U-tube GHEs, indicating the enhanced efficiency and superior performance of large-diameter thermal piles with augmented thermal capacity. Our findings suggest that GSHP systems with double-spiral-tube GHEs have superior efficiency than conventional GHEs.

The applicability of artificial neural networks (ANNs) and the adaptive neuro-fuzzy inference system (ANFIS) for determination of mean velocity and discharge of natural streams is investigated. The 2,184 field data obtained from four different sites on the Sarimsakli and Sosun streams in central Turkey were used in the study. ANNs and ANFIS models use the inputs, water surface velocity and water surface slope, to estimate the mean velocity and discharges of natural streams. The accuracies of both models were compared with the multiple-linear regression (MLR) model. The comparison results showed that the ANFIS model performed better than the ANNs and regression models for estimating mean velocity and discharge. The ANN model also showed better accuracy than the MLR model. The root mean square errors (RMSE) and mean absolute relative errors (MARE) of the MLR model were reduced by 88 and 91 % using the ANFIS model in estimating discharges, respectively. It is found that the optimal ANFIS model with RMSE of 0,063, MARE of 3,47 and determination coefficient (R2) of 0,996 in the test period is superior in estimation of discharge than the MLR model with RMSE of 0,532, MARE of 38,9 and R2 of 0,776, respectively. The study reveals that the ANFIS technique can be successfully used for estimating the mean velocity and discharge of natural streams by using only the inputs of water surface velocity and water surface slope.

The current study looks at the effectiveness of the removal of nickel (II) from aqueous solution using an adsorption method in a laboratory‐size reactor. An artificial neural network (ANN) and an adaptive neuro‐fuzzy inference system (ANFIS) were used in this study to predict blend hydrogels adsorption potential in the removal of nickel (II) from aqueous solution. Four operational variables, including initial Ni (II) concentration (mg/L), pH, contact duration (min), and adsorbent dose (mg/L), were used as an input with removal percentage (%) as the only output; they were studied to assess their impact on the adsorption study in the ANFIS model. In contrast, 70% of the data was used for training, while 15% of the data was used in testing and validation to build the ANN model. Feedforward propagation with the Levenberg–Marquardt algorithm was employed to train the network. The use of ANN and ANFIS models for experiments was used to optimize, construct, and develop prediction models for Ni (II) adsorption using blend hydrogels. The adsorption isotherm and kinetic models were also used to describe the process. The results show that ANN and ANFIS models are promising prediction approaches that can be applied to successfully predict metal ions adsorption. According to this finding, the root mean square errors (RMSE), absolute average relative errors (AARE), average relative errors (ARE), mean squared deviation (MSE), and R 2 for Ni (II) in the training dataset were 0.061, 0.078, 0.017, 0.019, and 0.986, respectively, for ANN. In the ANFIS model, the RMSE, AARE, ARE, MSE, and R 2 were 0.0129, 0.0119, 0.028, 0.030, and 0.995, respectively. The adsorption process was spontaneous and well explained by the Langmuir model, and chemisorption was the primary control. The morphology, functional groups, thermal characteristics, and crystallinity of blend hydrogels were all assessed.

Development of a ground-source heat pump (GSHP) system with higher efficiency, and evaluation of its operating performance, is essential to expand the growth of GSHP systems in Japan. A closed-loop GSHP system was constructed utilizing a flowing (artesian) well as a ground heat exchanger (GHE). The system was demonstrated for space-heating and space-cooling of a room (area 126.7 m2) in an office building. The average coefficient of performance was found to be 4.5 for space-heating and 8.1 for space-cooling. The maximum heat exchange rate was 70.8 W/m for space-heating and 57.6 W/m for space-cooling. From these results, it was determined that a GSHP system with a flowing well as a GHE can result in higher performance. With this kind of highly efficient system, energy saving and cost reduction can be expected. In order to assess appropriate locations for the installation of similar kinds of GSHP systems in Aizu Basin, a suitability map showing the distribution of groundwater up-flowing areas was prepared based on the results of a regional-scale three-dimensional analytical model. Groundwater up-flowing areas are considered to be suitable because the flowing well can be constructed at these areas. Performance evaluation of the GSHP system utilizing the flowing well, in conjunction with the prepared suitability map for its installation, can assist in the promotion of GSHP systems in Japan.

Thermal performance analysis of ground source heat pump system for low-temperature waste heat recovery storage