Fuzzy logic optimization of moisture content in boiler wood fuel used in tea factories in Kenya

This research investigates the use of fuzzy logic to optimize the moisture content of wood fuel used for boilers in Kenya's tea processing factories. Moisture levels are crucial as they influence combustion efficiency, energy production, and emissions levels. Integration of a fuzzy logic control system into the wood-fired boiler significantly reduced the moisture content from an initial 23% to a more optimal 18%, as determined by simulation outcomes. This 5% reduction in moisture content was accomplished through the dynamic adjustment of various boiler operating parameters, in this case, fuel feed rate, combustion airflow, and steam pressure, integrating a fuzzy logic algorithm that drew insights from both expert and real-time sensory data. The simulated operation of the fuzzy logic control system showed an enhancement in boiler efficiency of up to 81%, a decrease in emissions of up to 178g/kWh, and an overall improvement in system reliability, thereby demonstrating the efficacy of integrating fuzzy logic in wood-fired boiler for enhancing performance and addressing moisture-related challenges.