Gopal A. Krishnagopalan

Systematic reallocation of aqueous resources using mass integration in a typical pulp mill

Abstract This work deals with the application of mass integration to the Kraft process in an effort to minimize the fresh water usage. Due to the recycle and reuse of various aqueous streams present in the given process, there is a build-up of non-process elements (or NPEs) which has to be addressed. The approach consists of gaining process insights from the flow sheet via a graphical solution strategy followed by mathematical optimization. A targeting approach is described. The source sink diagram and path diagram are included in the analysis. A rule based on the lever arm and material balance is used to determine the sequence of steps at fresh water minimization. The graphical approach is easy to apply and allows the user to determine initial solutions to the problem. This is followed by a detailed mathematical optimization formulation for the problem and the solution strategy to be used to converge on the final solution. A case study dealing with the Kraft process, and chloride being the targeted NPE, is considered to demonstrate the potential benefits of the approach. Significant reductions in fresh water demands and wastewater discharges are observed for the Kraft process along with the allocation of NPEs. Any build-up of NPEs due to the recycle and reuse of various aqueous effluent streams to replace fresh water is accounted for via the inclusion of path diagram equations in the mathematical optimization. The principles developed are generic in nature and can easily be extended to other pulp processes as well.

Partial least squares (PLS) based monitoring and control of batch digesters

Abstract In this paper, a data-based control method for reducing product quality variations in batch pulp digesters is presented. Compared to the existing techniques, the new technique uses more liquor measurements in predicting the final pulp quality. The liquor measurements obtained at different time instances during a cook are related to the final pulp quality through a partial least squares (PLS) regression model. In using the PLS regression model for control, two approaches are proposed. In the first approach, optimal control moves are computed directly using the PLS model, while the second approach employs a nonlinear H-factor model of which parameters are adapted using the prediction from the PLS model. The effectiveness of the prediction and control algorithms is examined through simulation studies. Experimental study is then performed on a lab-scale batch digester, to test the effectiveness of the prediction performance of the PLS model. The control algorithms will be tested on the experimental set-up in the future.

PLS Based Monitoring and Control of Batch Digesters

Abstract In this paper, a data-based control method for reducing product quality variations in batch pulp digesters is presented. Compared to the previously proposed techniques, the new technique uses more on-line liquor measurements in predicting the final pulp quality. The liquor measurements obtained at different time instances are related to the final Kappa number through the PLS regression. In using the PLS regression model for control, two approaches are proposed: The first approach computes the optimal control moves directly from the PLS model while the second approach uses the prediction from the PLS model to adapt key parameters of a H-factor model. The effectiveness of the control algorithms is examined first through a simulation study and then through an experimental study performed on a lab-scale batch digester.