Enhancing Pulping Process Efficiency Through Fuzzy Logic Optimization

The paper production industry plays a crucial role in daily life across the globe, supplying paper for educational, industrial, and commercial uses. The manufacturing process typically involves two key stages. In the first stage, wood pulp—derived from trees—is treated with various fibers and chemical additives to enhance the physical properties of the final paper product. In the second stage, this treated pulp undergoes a drying and dewatering process within a paper-making machine. Central to this process is the pulping system, which comprises four main subsystems: the digester, knotter, decker, and opener. Notably, the knotter and opener subsystems are configured with two identical units—one operating and the other in cold standby mode to ensure continuous operation in case of failure. This paper presents a reliability-based availability analysis of the pulping system, assuming that the failure and repair rates of each subsystem are exponentially distributed. Given the uncertainties inherent in industrial environments and the frequent lack of precise historical data, all failure and repair rate parameters are modeled using triangular fuzzy numbers. A Markov process approach is employed to model the stochastic behavior of the system and determine its steady-state availability under fuzzy conditions. The analysis is further supported with a numerical example, demonstrating the applicability and effectiveness of the proposed fuzzy availability model in real-world industrial settings