Performance evaluation of optimal PI controller for ALSTOM gasifier during coal quality variations

• The performance of Alstom gasifier for coal quality variations is investigated.
• The baseline controller is tuned by Multi objective Particle Swarm Optimisation algorithm.
• TOPSIS algorithm chooses the best dominant values as controller constants.
• The designed controller meets the performance requirements at 0%, 50% and 100% loads.
• This paper provides best results for coal quality variations in comparison with recent proposals.

Coal gasifier, an essential part of Integrated Gasification Combined Cycle (IGCC) converts coal into synthesis gas (syngas or producer gas) under certain pressure and temperature. The quality of syngas is highly influenced by quality of coal (calorific value) and hence greatly affects the power generation. Gasifier control seems to be highly difficult since it involves many variables and inherent nonlinearity. The baseline PI controller provided with ALSTOM benchmark challenge II (benchmark model of coal gasifier) fails to satisfy the constraints at 0% load for sinusoidal pressure disturbance and coal quality variations (±18%). This paper evaluates the tuning parameters of ALSTOM benchmark challenge II using Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm. Robustness of the optimal PI controller is tested under sinusoidal and step pressure disturbance tests at 100%, 50% and 0% load conditions with decreased and increased coal quality variations. Test results show that the optimal PI controller meets all the constraints comfortably at all load conditions and provides better results for coal quality variations.