Applications of a model based predictive control to heat-exchangers (original) (raw)
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Predictive control applied to heat-exchanger networks
Chemical Engineering and Processing: Process Intensification, 2006
This paper discusses the online optimization and control of a heat-exchanger network (HEN) through a two-level control structure. The low level is a constrained model predictive control (MPC) and the high level is a supervisory online optimiser. Since MPC is a multivariable control technique capable of handling control-input constraints, it is neither necessary to define a variable-pairing approach nor to include individual loop-protections to avoid close-loop saturations. The proposed MPC algorithm uses an approximate linear model of the system to perform the output predictions and to account for the constraints. On the other hand, the supervisory program, based on a rigorous model, computes desired values to key manipulated variables of MPC, leading to minimum utility consumption. The coordination between the supervisory program and MPC is achieved through the definition of an extended cost-function that enables the controller to drive the system to the optimal operating condition. The proposed method was successfully tested by rigorous simulation of a typical HEN of the process industry.
Control and Operation of Heat Exchanger Networks using Model Predictive Control
Abstract—While, during the past decades, many strategies for heat exchanger network synthesis and design have been developed, much less effort have been dedicated to developing online optimal control strategies to tackle their complex and distributed dynamics. Since past optimal control design efforts predominately revolves around centralized control ideas they typically suffer from high computational demands.
Neural network predictive control of a heat exchanger
2010
The study attempts to show that using the neural network predictive control (NNPC) structure for control of thermal processes can lead to energy savings. The advantage of the NNPC is that it is not a linear-model-based strategy and the control input constraints are directly included into the synthesis. In the designed approach, the neural network is used as a nonlinear process model to predict the future behaviour of the controlled process with distributed parameters. The predictive control strategy is used to calculate optimal control inputs. The efficiency of the described control approach is verified by simulation experiments and a tubular heat exchanger is chosen as a controlled process. The control objective is to keep the temperature of the heated outlet stream at a desired value and minimize the energy consumption. The NNPC of the heat exchanger is compared with classical PID control. Comparison of the simulation results obtained using NNPC and those obtained by classical PID control demonstrates the effectiveness and superiority of the NNPC because of smaller consumption of heating medium.
Model predictive control based on neural networks for heat exchanger networks operation
Optimal operation of integrated heat exchangers is a challenge task in the field of process control due to system nonlinearities, disturbances and adequate model identification. This paper describes the design of an advanced neural network predictive control (NNPC) applied to a heat exchanger network. A case study with two hot and one cold streams, through three counter-current heat exchangers is used to test the proposed strategy. A lumped dynamic model is built based on the concept of multi-cells topology (mixed tanks), where the hot and cold cells are connected by a wall element throughout the heat exchanger length. Each cell is assumed perfectly mixed and all physical properties are constant. A distributed behavior is achieved by increasing the number of cells. The main assumptions of the lumped model are constant temperature in each cell, heat exchanger volume and area equally distributed between cells and negligible heat loss to the environment. The predictive controller relies on a neural-based model of the plant that is used to identify the system and to predict future performance over a predefined horizon. Results showed good control output regarding set point tracking.
Neural-network-based and robust model-based predictive control of a tubular heat exchanger
Chemical engineering transactions, 2017
The paper is devoted to advanced control of a tubular heat exchanger with focus to energy savings. The controlled tubular heat exchanger (HE) was used for petroleum pre-heating by hot water. The controlled output was the measured temperature of the petroleum in the output stream and the control input was the volumetric flow rate of hot water. Two advanced control strategies were investigated in the set-point tracking, the neural-networkbased predictive control and the robust model-based predictive control with integral action and with soft constraints on control inputs. The advanced control of the heat exchanger was implemented in the MATLAB/Simulink simulation environment. Simulation results obtained using advanced controllers were compared with the results ensured by a conventional PID controller and they confirmed significant improvement of the control performance. Moreover, advanced controllers reduced energy consumption measured by the total consumption of hot fluid used for he...
Indonesian Journal of Electrical Engineering and Computer Science
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost interface using X-transposed-region (XTR) converter, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished...
IMC-PID and Predictive Controller Design for a Shell and Tube Heat Exchanger
2009 Second International Conference on Emerging Trends in Engineering & Technology, 2009
The objective of this work is the development of DMC for a shell and tube heat exchanger and to address the difficulties in tuning a DMC. Although the PID controller is widely used for these types of applications there is still a need for optimization of conservation of energy. In this paper a model based predictive algorithm is used for controlling a temperature of a fluid stream using the shell and tube heat exchanger and the associated difficulties in tuning are analyzed. The transients and steady state results obtained using a DMC is compared with a conventional PID and IMC-PID controller.
Adaptive Generalized Predictive Control of a heat exchanger pilot plant
2011 International Conference on Multimedia Computing and Systems, 2011
In this paper, the Adaptive Generalized Predictive Control is designed to control a heat exchanger pilot plant. The standard Generalized Predictive Control (GPC) algorithm is presented. The Adaptive Generalized Predictive Control is then applied to achieve set point tracking of the output of the plant. A Single Input Single Output (SISO) model is used for control purposes. The model parameters are estimated on-line using an identification algorithm based on Recursive Least Squares (RLS) method. The performance of the proposed controller is illustrated by a simulation example of a heat exchanger pilot plant. Obtained results demonstrate the effectiveness and superiority of the proposed algorithm.
Robust model predictive control of a plate heat exchanger
Chemical engineering transactions, 2018
Advanced process control includes optimization-based tools that are recently widely implemented in industry to maximize economical effectiveness and to minimize environmental impact. Robust model predictive control (MPC) is one of these strategies and it combines benefits of model predictive control and robust control approaches. This study investigates improvement of control performance and increase of energy savings using the soft-constrained robust MPC with integral action for a laboratory plate heat exchanger. Soft constraints on control inputs keep the heat exchanger in required operation conditions and enable to use the feasible range of manipulated variable effectively with decreasing of energy cost. Integral action of the predictive controller ensures offset-free reference tracking. Simulation results obtained using the newly designed robust predictive controller with soft constraints and integral action confirm improved control response and increased energy savings in compa...
Highlights: 1. Nonlinear control requires a model, often developed with nonlinear regression. 2. Model predictive control (MPC) needs to optimize the time-sequence of future manipulated variable inputs to the model. 3. This work uses a novel Leapfrogging optimizer for both applications, and 4. Demonstrates nonlinear MPC on a pilot-scale heat exchanger. Abstract: This work reveals the applicability of a relatively new optimization technique, Leapfrogging, for both nonlinear regression modeling and a methodology for nonlinear model-predictive control. Both are relatively simple, yet effective. The application on a nonlinear, pilot-scale, shell-and-tube heat exchanger reveals practicability of the techniques.