Mario Richard Eden

Softwood kraft pulping is constrained by the competing requirements of efficient delignification and preservation of carbohydrate yield. This study investigated the effect of a novel phenolic cooking additive (2,4-xylenol) on pulping selectivity and lignin structural modification during softwood kraft pulping. Pilot-scale kraft cooks were conducted under identical cooking conditions, with and without the additive, using a modern batch digester. Key process metrics, including pulp yield, rejects, and kappa number, were measured, and compositional analysis was performed on the kraft unbleached pulps. Lignin samples isolated from black liquor and residual pulp were further characterized using 31P and two-dimensional heteronuclear single quantum coherence (2D HSQC) nuclear magnetic resonance (NMR) and gel permeation chromatography (GPC). Additive-assisted cooks reduced the pulp kappa number by approximately 3–4 units relative to the control while maintaining pulp yield, when 0.5 wt.% -0.75wt.% (based on oven-dry wood) of the additive was dosed at 50% cook completion or later. Compositional analysis showed that glucan content remained largely unchanged, whereas mannan retention increased substantially in additive-treated pulps, indicating enhanced preservation of glucomannan during kraft cooking. Component mass-balance analysis confirmed the simultaneous enhancement of lignin removal and carbohydrate retention in additive-assisted cooks. NMR analysis indicated altered lignin structural characteristics and increased carbohydrate-associated signals in additive-derived lignins. GPC analysis further revealed changes in lignin molecular weight distribution consistent with enhanced lignin fragmentation and reduced condensation. Overall, the additive improved kraft pulping selectivity by simultaneously promoting lignin removal and suppressing glucomannan degradation, demonstrating strong potential for improving the efficiency of softwood kraft pulping.

This paper presents a machine learning (ML) approach to predict the potential health issues of solvents by uncovering the hidden relationship between substances and toxicity. Solvent selection is a crucial step in industrial processes. However, prolonged exposure to solvents has been found to pose significant risks to human health. To mitigate these hazards, it is crucial to develop a predictive model for health performance by identifying the contributing factors to solvent toxicity. This research aims to develop a predictive model for health issues related to solvent toxicity. Among various algorithms in ML, Rough Set Machine Learning (RSML) was chosen for this work due to its interpretable nature of the generated models. The models have been developed through data collection on the toxicity of various organic solvents, the construction of predictive models with decision rules, and model verification. The results reveal correlations between solvent toxicity and the Balaban index, valence connectivity index, Wiener index, and boiling points. The generated predictive model using RSML has successfully provided insightful observations about the correlation between human toxicity and molecular attributes.

In this review paper, a brief overview of the increasing applicability of Process Systems Engineering (PSE) tools in two research areas, which are the design of ionic liquids and the design of integrated biorefineries, is presented. The development and advances of novel computational tools and optimization approaches in recent years have enabled these applications with practical results. A general introduction to ionic liquids and their various applications is presented followed by the major challenges in the design of optimal ionic liquids. Significant improvements in computational efficiency have made it possible to provide more reliable data for optimal system design, minimize the production cost of ionic liquids, and reduce the environmental impact caused by such solvents. Hence, the development of novel computational tools and optimization tools that contribute to the design of ionic liquids have been reviewed in detail. A detailed review of the recent developments in PSE applications in the field of integrated biorefineries is then presented. Various value-added products could be processed by the integrated biorefinery aided with applications of PSE tools with the aim of enhancing the sustainability performance in terms of economic, environmental, and social impacts. The application of molecular design tools in the design of integrated biorefineries is also highlighted. Major developments in the application of ionic liquids in integrated biorefineries have been emphasized. This paper is concluded by highlighting the major opportunities for further research in these two research areas and the areas for possible integration of these research fields.