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

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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