Control of batch pulping process using data-driven constrained iterative learning control

Kraft pulping uses steam and chemicals to convert wood chips to pulp, used for production of paper, textile fiber or other cellulosic materials, in large batch or continuous digesters. Batch digesters owe their widespread use to lower capex as well as flexibility of operation that can easily accommodate changes in feedstock or product grades. However, control of batch pulping process is challenging since wood is a naturally-occurring reactant. Moreover, lack of real-time measurements of key performance indicator such as Kappa number and lack of high-fidelity models make the batch control difficult to implement. One solution consists of using run-to-run control that uses offline laboratory measurements to provide a recipe for the upcoming batch. In this work, a first-principles dynamic model is developed for manufacture of dissolving pulp for textile fiber from wood. Subsequently, a data-driven constrained Iterative learning control (cILC) technique is explored for Kappa number control via manipulation of recirculation liquor temperature. Data-driven cILC uses a linear time varying model of the batch pulping process that is developed and updated from historical data. Constraints are imposed on batch-to-batch change in the inputs to prevent sharp fluctuations of the process state and output variables between batches. The data-driven cILC solves a multivariable linear regression followed by a quadratic program to generate a batch recipe for the next run. Three case studies are presented to evaluate performances for target Kappa tracking, and deterministic and stochastic uncertainty handling. The results demonstrate that data-driven cILC is effective in control of the pulp digester, even in presence of uncertainties, without the need for expensive real-time measurements.