Control of a Multiple-Effect Falling-Film Evaporator Plant

Abstract

This work studies the control of a single falling-film evaporator and a multiple-effect fallingfilm evaporator plant. The specific case studied is the concentration of solids found in the liquor that is part of the production of sulfate (kraft) pulp. The concentrated stream is the feed to the recovery boiler, a unit operation that plays a significant role in the economics of the pulp mill. The disturbance compensation of a single-loop strategy based on proportional-integral feedback controllers is compared against a model-based strategy that uses optimal control theory.

Measurement and process control for water and energy use in the food industry

“…Several researchers have studied the development of a process model and the design of a high-efficiency control system (Winchester and Marsh, 1999;Stefanov and Hoo, 2005;Bakker et al, 2006;Karimi et al, 2007). Significant energy savings can be achieved with a good design of the process and a suitable control system.…”

Water cycle algorithm tuned robust fractional‐order Proportional–Integral–Derivative controller for energy optimization and control of nonlinear Multiple Stage Evaporator: A case study of paper mill

“…This assists in the implementation of a tight and suitable control algorithm by effective utilization of simulation model instead of the real plant. Literature works reveal that the nonlinear dynamic model of MSE has been simulated for designing various controllers such as conventional Proportional-Integral-Derivative (PID), 18,19 Cascade-PID, 20 model predictive control (MPC), 21,22 linear quadratic regulation (LQR) 23 or flatness-based control, 24 and Fuzzy-PID. 2,25 However, some precarious issues in the model simulation and controller design still exist, for instance, the premature convergence of the optimization techniques, computational complexity, and the behavior of the controller to noise and disturbances.…”

Modular simulation model for falling film evaporators with novel approach to manage dominant time-varying transport delays

“…Also, novel developments concerning control concepts or system designs can be easily tested in a valid simulation environment, since there is less need for extensive experiments. In particular, instead of widespread PID control approaches (O'Callaghan and Cunningham, 2005), modern control concepts based on the mathematical model such as cascade control (Bakker et al, 2006), model predictive control (MPC) (Quaak et al, 1994;Stefanov and Hoo, 2005), linear quadratic regulation (LQR) (Haasbroek et al, 2013) or flatness-based control (Lvine, 2009) can be established. Among others, the latter facts indicate the significance of developing mathematical models to simulate the FFE process.…”

“…In 1990, Tonelli et al (1990) presented a computer package, where constant time-delays in and between the effects could be included into the simulation model. Further recent studies on dynamic FFE simulation focus on detailed subsystem modeling (Quaak and Gerritsen, 1990;Winchester, 2000;Paramalingam, 2004), usage of dynamic models for control design (Winchester and Marsh, 1999;Bakker et al, 2006;Stefanov and Hoo, 2005) or distributed-parameter effect models (Stefanov and Hoo, 2004;Bojnourd et al, 2015). In all of these full plant simulation models, constant transport velocity and thus constant delay is assumed.…”