Strength enhancement of fiber network by carboxymethyl cellulose during oxygen delignification of kraft pulp
Sorption of carboxymethyl cellulose (CMC) on the fiber surface was applied during oxygen delignification to enhance the strength properties of softwood kraft pulp. Unlike many previous efforts, the focus was not set on the improvement of selectivity of oxygen delignification, i.e. retaining stable viscosity vs. decreasing kappa number. Instead, without an improved selectivity, handsheets from CMC-treated fibers exhibited a 15% improvement in tensile index and 25% improvement in tear index after a full bleaching sequence in comparison to the untreated reference pulp. Since it is demonstrated that the CMC addition can be incorporated as an integral step in the fiberline process, the method offers an effortless and viable option to produce pulp resulting in stronger paper products.
The impact of cellulose fibre surface modification on some physico-chemical properties of the ensuing papers
Abstract This review covers both physical and chemical modifications of cellulose fibre surface and their impact on the properties of the ensuing papers. It is not restricted to actual industrial operations, but also deals instead with ongoing research in this area, which appears promising in terms of possible applications to papermaking.
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.