Adaptive Predictive Expert Control Research Articles

Adaptive predictive expert control of superheated steam temperature in a coal‐fired power plant

SUMMARYThis paper describes the application of an adaptive predictive expert (ADEX) control solution to the attemperation process for superheated steam temperature control in the ScottishPower coal‐fired power station at Cockenzie near Edinburgh in Scotland. The project comprised two phases, the first of which was a trial to establish the feasibility and potential benefits of ADEX compared with the existing control system by using a software platform on PC connected to the plant Programmable Logic Controller (PLC) via Object linking and embedding for Process Control (OPC), and the second phase was to verify and install a permanent, robust solution using an ADEX controller module with a digital link to the plant PLC. The overall objective of the project was to reduce steam temperature variations caused by load changes and other perturbations in order to protect the steam turbine (Unit 3) and to minimize thermal stresses in the attemperation pipework and boiler and so minimize premature failures along with associated cost risk. In the first phase, ADEX demonstrated a 50% improvement in reducing steam temperature variation overall compared with the existing method, and the subsequent installation of the ADEX controller module provided the necessary robustness and backups for a final industrial solution. The results of both phases are displayed graphically. Copyright © 2012 John Wiley & Sons, Ltd.

Adaptive predictive expert control of levels in large canals for irrigation water distribution

SUMMARYProportional integral (PI) control is the most frequently used method for controlling water levels in irrigation canals. For design purposes, simplified linear models are typically used to describe the series of pools that, separated by control gates, compose the whole canal. Common models are made by series connection of integrator plus time delay or integrator with time delay plus a zero, these models being obtained from the Saint‐Venant equations. However, it is known that hydraulic processes in irrigation canals are strongly nonlinear and present dynamics that are time varying depending on the operating point and the hydraulic conditions of the pool, so that the performance of PI controls may be reduced as the operating point moves far from the nominal design state. This paper presents the application of an adaptive predictive expert control (ADEX) for the distant downstream level control in irrigation canals trying to exploit their adaptive capability to efficiently achieve changes in the setpoint levels and disturbance rejection. Both PI and ADEX controllers are compared in simulation and in real field tests in the control of the end pool of the Canal Imperial de Aragón in Spain. Copyright © 2012 John Wiley & Sons, Ltd.

Adaptive control of the oxidation ditch reactors in a wastewater treatment plant

SUMMARYThis paper describes the application of adaptive predictive expert (ADEX) control methodology to the oxidation ditch reactor in the wastewater treatment plant of Ceutí (Murcia, Spain) and evaluates the performance of the ADEX control system against that of the previous control system operating the plant. After a basic description of ADEX control methodology, the ditch reactor is described, as well as the control objectives and the control strategy, which focused on energy consumption reduction. The results of the application of the ADEX control system show significant improvement compared with the previous control system in terms of higher stability and precision of controlled variables, reduction of energy consumption of the plant, and better quality of the effluent water. Copyright © 2012 John Wiley & Sons, Ltd.

Adaptive predictive control of the sulfur recovery process at Pemex Cadereyta refinery

SUMMARYThis paper describes the design and application of an optimized control system, based on adaptive predictive expert control methodology, to the sulfur recovery units (SRUs) of the Pemex Refinery in Cadereyta, and presents comparative results obtained in relation to those of the conventional control system operating the plant. The sulfur recovery process represents the final stage in the refinery production chain, and is responsible for removing the sulfur content, usually in the form of hydrogen sulfide (H2S) waste gas generated by other refinery processes. The main goal is minimizing the sulfur content in the gasses released into the atmosphere to comply with emission level requirements. This goal can be obtained by a precise control of the ratio between hydrogen sulfide and sulfur dioxide (SO2) in the tail gas, which determines, through a Claus reaction, the maximum sulfur recovery. The SRUs operating in Cadereyta were connected to a common amine acid gas collector and a common ammonia acid gas collector. Optimized control strategies were designed to control the pressure on both acid gas collectors and the flow rate of air entering each SRU. The results show that the H2S:SO2 ratio in the tail gas is considerably more stable under optimized control, particularly in the presence of abrupt changes in the acid gas load. The peaks observed in this ratio under conventional control disappear under optimized control showing a significant improvement in the process operation. Copyright © 2012 John Wiley & Sons, Ltd.

Adaptive predictive expert control of dissolved oxygen concentration in a wastewater treatment plant

In this paper we present the application of adaptive predictive expert controllers to dissolved oxygen (DO) control in the aerobic reactors of a wastewater treatment plant. The control system described in this paper consists of adaptive predictive expert control loops complemented by optimisation logic. The controllers successfully cope with nonlinearity and changing operating conditions of the process by predicting the evolution of the controlled variable and adapting to changes in the process dynamics. This results in more precise and stable DO control, offering many benefits. The complementary optimisation logic maintains the air pressure in the common collector at the lowest possible level, enabling adequate DO control and thus considerably reducing energy consumption.