Jukka Kortela

Fault propagation analysis of oscillations in control loops using data-driven causality and plant connectivity

Abstract Oscillations in control loops are one of the most prevalent problems in industrial processes. Due to their adverse effect on the overall process performance, finding how oscillations propagate through the process units is of major importance. This paper presents a method for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.

Model predictive control for BioPower combined heat and power (CHP) plant

Abstract This paper presents a model predictive control (MPC) development for BioGrate boiler. Amount of fuel and moisture in the furnace are chosen as the state variables for the MPC model in order to take into account fuel quality. To this end, dynamic models for fuel decomposition and water evaporation in the furnace are used. As a result, the drum pressure can be predicted accurately and an efficient stabilization of the plant operations is possible by using the MPC. The performance of the MPC is evaluated using real industrial plant data and compared with the currently used control strategy. Finally, the results are presented, analyzed and discussed.

Fuel quality soft-sensor for control strategy improvement of the Biopower 5 CHP plant

This paper aims to present an enhanced method for estimating fuel quality on a BioGrate combustion process and its use in a control strategy improvement. The dynamic model based method (DMBM) utilizes combustion power which is calculated using the oxygen consumption of the furnace and the energy balance of the boiler. The proposed method is tested with data from the industrial scale Biopower 5 CHP plant and compared with the method currently used in industry, and finally the results are analyzed and discussed.

Fault-tolerant model predictive control (FTMPC) for the BioGrate boiler

The fuel bed height sensor is a critical element in the control of the BioGrate boiler. A fault appearing in this sensor greatly affects the control performance in the sense that air distribution in the BioGrate boiler deviates from its nominal distribution. To address this problem, a fault tolerant model predictive control (FTMPC) has been developed to accommodate the fault in this fuel bed height sensor by the active controller reconfiguration. In this fault tolerant strategy, water evaporation in the furnace is estimated by fuel moisture soft-sensor, and thermal decomposition of dry fuel is estimated by utilizing oxygen consumption. This renders the power output of the boiler to be accurately predicted and controlled. The proposed FTMPC is successfully tested with the BioPower 5 CHP plant data and the results are presented, analyzed, and discussed.

Fault propagation analysis by combining data-driven causal analysis and plant connectivity

This paper presents a novel technique for integrating process causality and topology which ultimately enables to determine the propagation path of oscillations in control loops. The integration is performed using a dedicated search algorithm which validates the quantitative results of the data-driven causality using the qualitative information on plant connectivity extracted from a piping and instrumentation diagram. The outcome is an enhanced causal model which reveals the propagation path. The analysis is demonstrated on a case study of an industrial paperboard machine with multiple oscillations in its drying section due to valve stiction.

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation, ETFA 2016

For improving the load-following capacity of existing grate boiler units, an MPC control concept based on the combustion power soft-sensor is developed. Because the combustion power estimation has a very quick response to the primary air flow input, the load-following speed of the boiler control system will be improved considerably. The proposed MPC strategy is tested with the BioPower 5 CHP plant data and the results are presented, analyzed, and discussed.

Fault Tolerant Model Predictive Control for the Biopower 5 CHP Plant

The main aim of control of the BioGrate boiler is stable energy production, where a fuel bed height sensor is a critical element in the control of the BioGrate boiler and its faulty operation should thus be avoided. A fault tolerant model predictive control (FTMPC) has been developed to accommodate the fault in this fuel bed height sensor by active controller reconfiguration. The proposed FTMPC is tested with the BioPower 5 CHP plant data by simulation and finally the results are presented, analysed, and discussed.