Improved Profitability of a Pulping Mill through Progressive Controller Structure Expansion

Abstract

It has been well documented that multivariate systems will benefit from application of advanced control strategies. The application of these methods has thus far been hindered for a variety of reasons which range from complexity of the design methodologies to the cost associated with implementation of these solutions. This paper shows that there exist alternative means by which existing control strategies of a multivariate system could be extended, thereby leading to increased efficiency, without the need to remove and implement a new control system installation. A realistic model of an entire pulping mill is used in this paper to show that by careful selection of only a few additional control loops which are added to the existing structure, sizeable profitability and operational gains may be achieved.

Automated Control Configuration Selection Considering System Uncertainties

“…Notice that (14) will be used to satisfy the pairing rules (a) and (b) for the uncertain systems by avoiding the pairs correspond to φ P ij ≤ −1 while the upcoming equations (21) and (22) which are derived from (18) are the ones to be used to verify the validly of the optimal configuration obtained from the nominal system (G) under the effect of the uncertainty.…”

“…s6. Calculate P ij and P ij by (21) and (22) s7. Formulate the vertices (v's) as in (23) s8.All v's satisfy θ…”

“…Step 6 of the proposed approach is to calculate the bounds of the P 's employing (21) and (22) except for those correspond to φ P ij ≤ −1. The bounds are to be used to determine the vertices to be utilized in the verification process.…”

“…Obtaining the perturbation set (θ) ends step 5 of the proposed approach. In step 6, the bounds in (21) and (22) are calculated to formulate the vertices of the polytopic model as in step 7. For the uncertain system Therefore, the optimality of the configuration selection obtained from the nominal system G given by (30) is not guaranteed with respect to the perturbed system G P given by (33).…”

Design and assessment of variable-structure LQG PID multivariable controllers

“…Progress on this route has been made by utilizing the results from signal processing and machine learning based on the l 1 norm heuristic and its sparsity inducing property. In , the control structure selection is studied for sparse static pre‐compensators . The simultaneous selection and optimization of the values of nonzero ‘static gains’ is formulated into a quadratic programme with an additional sparsity inducing l 1 norm term.…”