Using Particle Swarm Optimization with Backpropagation Neural Networks and Analytic Hierarchy Process to Optimize the Power Generation Performance of Enhanced Geothermal System (EGS)

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Using Particle Swarm Optimization with Backpropagation Neural Networks and Analytic Hierarchy Process to Optimize the Power Generation Performance of Enhanced Geothermal System (EGS)


Figure 5 shows the evolution of production temperature ( T p r o ), electricity generation rate ( W e ), injection pressure ( P i n j ) and electric energy efficiency ( η e ) of the base model during a period of 30 years. The T p r o decline process can be divided into two stages. In the stable stage (0–10 years), T p r o decreased by less than 1%. In the decline stage (10–30 years), the average annual T p r o decreased by more than 1%. T p r o decreased from the initial 192.5 °C to 169.1 °C (reduced by 12.2%) in the period of 30 years. The trend of W e was approximately the same as that of T p r o ; W e decreased from 5.0 MW to 3.9 MW (reduced by 21%), which was too large and required further adjustment of the operating parameters to optimize the EGS. During the operation of the system, P i n j increased from 54.8 MPa to 62.2 MPa, with an average value of 60.6 MPa. P i n j was less than the minimum principal stress ( σ h min ) of the reservoir and met the design requirements. η e during the operation of the system is 3.9. In the early stage of the system operation, the rapid increase of pore pressure at the injection well causes a sharp increase in internal energy consumption, resulting in a sharp drop in η e .
As depicted in Figure 5a–d, distinct factors exert varying influences on different performance indicators. k , q and d exhibit a greater impact on T p r o ; k , T i n j and q demonstrate a stronger influence on P i n j ; k , T i n j , q and d have a more pronounced effect on W e ; while k , T i n j and q possess a higher degree of influence on η e . Increasing k is more favorable for fluid flow in a thermal reservoir, which can effectively reduce P i n j and W p , so it is beneficial to increase η e . Meanwhile, increasing k shortens the continuous extraction time of reservoir thermal energy, so the decline speed of T p r o and W e is accelerated. An increase in q will lead to an increase in the extraction of heat from the hot reservoir, and the reservoir temperature will decrease accordingly. T p r o will decrease significantly with the increase of q , while W e will increase significantly. Higher q means more fluid enters the heat exchange channel, resulting in higher P i n j and W p at the bottom of the water injection well, and therefore, η e will also decrease accordingly. The viscosity of water increases as T i n j decreases, leading to higher P i n j . The augmented viscosity hampers fluid circulation within the reservoir, resulting in a rise in W p and consequently causing a decline in η e . According to Equation (3), a decrease in T i n j will result in an increased enthalpy difference between the injected and produced fluids, leading to a significant increase in W e . Increasing d means a larger reservoir volume, so more geothermal energy is stored between the injection well and the producing well, so both T p r o and W e increase. At the same time, as d increases, the flow path from the injection well to the producing well will also be extended, resulting in higher P i n j .

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