This study presents a statistical modeling approach for wood consumption in the cellulose industry. One of the goals of the cellulose industry is to identify the factors that can affect wood consumption (CEM) and cause high variability in production costs. Multiple linear regression models were used for this purpose. After the factors of influence were identified (for optimal conditions), the goal was to determine their appropriate values to minimize CEM in the production process, using response surface methods. The results showed that three factors affect wood consumption: the basic density (DB) of the wood received, the Kraft pulp yield (REND), and wood losses that occur during the production process (PERDAS). The optimal conditions identified are DB > 500 kg-dry/m³-solid, REND > 57% and PERDAS < 13%. The statistical approach taken in this work is concluded to be useful in process control in the cellulose industry and can also provide insights useful to forest planning.
Multiple linear regression; Cellulose production; Response surface analysis; Pulp wood specific consumption
