Pradeep B. Deshpande

NONLINEAR PH CONTROL

A simple new method for designing nonlinear IMC controlles for SISO systems has been developed. The method has been applied to the neutralization of a simulated strong acid-strong base system. The objective of the control effort in this instance is to maintain the effluent pH at 7.00 in the presence of disturbances. An examination of the results shows that the controller provides perfect set point compensation and excellent disturbance rejection. The results also show that to implement this type of controller for pH control a fast CPU with extended precision capabilities and a fast analog-to-digital converter would be required.

Advanced control of a reverse osmosis desalination unit

Abstract An experimental investigation of constrained model predictive control (CMPC) for a reverse osmosis (RO) desalination unit has been conducted. For comparison purposes, results with traditional PID-type control have also been obtained. The experimental unit consists of a series of four cellulose acetate membranes. A 486-PC is used as the data acquisition and control computer. It is interfaced to the experimental unit via analogue-to-digital and digital-to-analogue converter boards. The models required for CMPC and PID-type controls are obtained by step testing. The RO system has four outputs and two inputs. The outputs are (1) permeate flow rate, (2) permeate conductivity, indicative of the salt content in the product, (3) trans-membrane pressure, and (4) inlet pH. The inputs are (1) flow rate of reject water and (2) inlet acid flow rate. The production objectives are to produce the specified flow rate of permeate, having the desired salt content, subject to the constraints that the inlet pH and the trans-membrane pressure are within specified bounds. It is shown that CMPC can achieve these goals. It is also demonstrated that CMPC can maximize the throughput subject to the constraints on the other three outputs. A comparison of the results with CMPC and PI control reveals the excellent capability of CMPC for RO desalination plant operations.

Enhancing the robustness of internal-model-based nonlinear pH controller☆

A simple mechanism that renders robustness to a nonlinear controller designed using an internal-model control (IMC) strategy is presented. A comparative study between this and the conventional IMC strategy for controlling the pH of a strong acid—strong base solution shows five orders of magnitude improvement in the extent of sampling time that can be tolerated. The simulations also suggest speedier recovery of system to the set point in the presence of load disturbances and modeling errors.

Advanced process control of a B-9 Permasep® permeator desalination pilot plant

An experimental application of advanced control and optimization on a hollow-fiber membrane module (B-9 Permasep® permeator by DuPont) is presented. The objective of the study was to compare the performance of standard proportional-integral (PI) control with the performance of a constrained model predictive control (CMPC). A proper control strategy, whether PI or CMPC, should allow for the manipulation (servo control) of the product flow rate while maintaining product quality. In doing so, a plant can adjust the production of water to meet demand. Several PI control experiments involving set point changes in product flow rate and conductivity (a measure of quality) were conducted. It was found that PI control was unable to properly control the quality of the product by means of manipulating the pH of the feed. The PI controller over-compensated for offset in product conductivity. In contrast, CMPC displayed superior performance in the control of the pilot plant by holding the process outputs within specified bounds; especially the feed pH which prevented the conductivity PI control loop from becoming unstable. Furthermore, CMPC was able to maximize the product flow rate by 13.6% while improving the conductivity (quality) by 1.1%.

Advanced controls for multi-stage flash (MSF) desalination plant optimization

Abstract An in-depth study of an MSF desalination plant control system has been conducted. The degrees of freedom analysis based on a dynamic mathematical model was used to determine the number of controlled (PVs) and manipulated variables (MVs). The analysis of the control problem points to a fully interacting multivariable system. Furthermore, prudent plant practices dictate strict bounds on several of these PVs and MVs and the desire to achieve unit optimization is felt. Thus, the MSF plant is an ideal candidate for constrained model predictive control (CMPC). A locally developed CMPC was designed to achieve a variety of operational objectives such as maximizing distillate production or performance ratio, minimizing energy consumption, etc. CMPC provides set points to the existing PID controllers and, thus, integrity with the existing instrumentation is maintained. The performance of CMPC was tested utilizing the SPEEDUP dynamic simulation software and the results are excellent. For example, it is shown that for an illustrative plant, steam savings of

Neural networks for the identification of MSF desalination plants

1.6 million per year are possible. The potential impact of CMPC for MSF plants is rather large considering that there are several hundred plants throughout the world all of which are currently on PID-type control.

Experimental application of robust nonlinear control law to pH control

Fully connected multi-layer feedforward artificial neural networks trained using the error-back-propagation algorithm have been employed to identify the nonlinear multi-variable, multi-stage flash (MSF) desalination plant. Both multiple input-single output (MISO) and multiple input-multiple output (MIMO) networks have been used for the purpose of identification. The correlation coefficient values greater than 0.99 were obtained suggesting that the neural network can serve as a good alternative to a model MSF desalination plant.

On dynamic control of chaos: a study with reference to a reacting system

An experimental application of RNCL to pH control is presented. The experimental setup consists of a computer controlled CSTR (continuous stirred tank reactor) and the control objective is to maintain the pH of a strong-acid strong-base system at the neutralization point in the presence of disturbances. The algorithms considered are PI (proportional-integral) control, nonlinear IMC (internal model control), and RNCL (robust nonlinear control law). The experimental results point to the excellent capability of RNCL for controlling pH comparison to nonlinear IMC and PI control.

ROBUST NONLINEAR CONTROL WITH NEURAL NETWORKS

A method for dynamic control of chaotic systems has been developed. The application of this method for a representative problem, namely, that of a nonisothermal consecutive chemical reaction in a continuously stirred tank reactor shows its excellent capability for servo and regulatory control in the presence of deterministic and stochastic disturbances.

Dynamic matrix control of an industrial steam gas reformer

A new method for robust nonlinear control of single-input single-output systems is presented. The control law utilizes the universal approximation characteristic of neural networks augmented with the ability for adaptation. The presence of neural networks obviates the need for a mechanistic model for control law computations and the difficulties associated with model-based approaches become irrelevant. The new control law called N-RNCL incorporates the ability for adaptation through an adjustment of bias neurons and ensures offset-free performance in the presence of load and unmeasured disturbances. The performance of N-RNCL is demonstrated using the examples of a strong-acid strong-base pH control system and a nonlinear heat exchanger system. The state-of-the-art controller shows excellent servo and regulatory performance.