One of the key properties of paper is its breaking length, which is usually controlled in many paper products. To achieve this, several natural and synthetic polymers are used in paper industries in accordance with paper grades and customer needs. In this study, the combination of cationic starch (CS) and/or polyacrylamide (CPAM) as common additives, and cellulose nanofibers (CNFs), were added to a short-fiber pulp suspension to investigate the reinforcement effects and to compare such properties with those of paper prepared with 20% softwood long-fiber. The breaking length was measured on the prepared handsheets. The results showed that adding 1% CS significantly improves paper breaking length, which was well comparable to the handsheets reinforced with 15% softwood pulp. The results showed that adding less CS (0.5%) along with 3% CNFs significantly increased the paper breaking length, while reducing process difficulties associated with CS. The same result was also achieved adding 3% CNFs along with 0.03% CPAM. Furthermore, a triple system of CNFs, CS, and CPAM additives significantly enhanced the paper breaking length and surpassed the breaking length of paper made with 20% softwood pulp. Using this triple system led to the least changes in handsheet thickness as well. Therefore, this triple system of additives can replace softwood pulp, thereby significantly expanding the spectrum of paper products for the countries with limited softwood pulp sources.

Pulp and paper industries have provided great research opportunities to control systems. The objective of this study was to investigate the relationships between 80 process variables of CMP tower and stock preparation, and 17 newsprint quality properties in Mazandaran Wood and Paper Industries (MWPI). After the preparation of two suitable data series considering the time needed for pulp to paper, the relations between process dependent and newsprint independent variables were determined using partial least squares (PLS) regression. As a result, two PLS models were developed. The fi rst model with 4 latent vectors categorized and related CMP tower variables and the second one, through 8 latent vectors connected stock preparation variables with paper properties. PLS regression coeffi cients determined how much the most infl uencing process variables impact each paper properties.