Towards Digital Twin and Augmented Reality Modelling to Mitigate Silica Scaling in Geothermal Plants

Silica scaling in geothermal reinjection reduces efficiency, increases costs, and causes downtime. An elevated concentration of silica is experienced in the separator tank after depressurizing the brine. This causes amorphous silica to exceed its solubility limit, causing supersaturation, clogging and disrupting steam production. The study investigated the use of digital twins, augmented reality, and Industrial Internet of Things technologies for remote real-time monitoring and control of silica treatment. A laboratory-synthesized geothermal fluid with an initial concentration of 90 mg/L was prepared. Silica concentration was determined using the molybdate method with an MRC UV/VIS16-spectrophotometer. A response surface methodology based on Box Behnken was used to develop a model for optimal conditions of silica precipitation. Analytic Process Control System (APCS) system using PLC was used to monitor and control synthetic geofluid pH, volume and temperature while silica precipitation was observed. 0.9M of NaOH was introduced to geofluid to maintain a setpoint, pH11. The AR-DT model was used for real-time remote monitoring and control to avert silica scale buildup at pH11. Maximum silica extraction of 96.38% was observed at pH7.5, temperature of 80°C, volume of 5.5 L and lowest silica extraction of 66.90% was observed at pH5.5, temperature of 50°C and volume of 5.5L. The DT and AR model effectively simulated geofluid behaviour, communicating with APCS using IIoT via MQTT protocol, enabling real-time data exchange. Deviations in pH from setpoint triggered corrective action based on using historical and real-time data, sending a signal to the PLC to adjust the NaOH solution dosage. The AR model provided an immersive experience, enhancing monitoring and control efficiency.