Multivariate measures of in-process wood pulp composition and morphology: smart sensors for real-time process control and fibre product optimization

过程中木浆成分和形态的多变量测量：用于实时过程控制和纤维产品优化的智能传感器

• 批准号: 494643-2016
• 负责人: Grant, EdwardR
• 金额: $ 4.2万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=652324
• 关键词: Multivariate; measures; process; wood; pulp

Bleached kraft pulps derived from Canadian northern softwood lead the world in terms of fibre length, strength, fineness and uniformity. However, progress in paper manufacturing technology has narrowed the advantage of NBSK pulps, increasing price-performance pressure on Canadian suppliers. To remain competitive, the Canadian pulp industry must innovate, developing new processes and process-control strategies that yield pulps of higher quality and lower cost, with reduced use of energy and impact on the environment. The chemical and material properties of a pulp determine the physical properties of the fibre network it produces, and with adequate process monitoring and intervention, the opportunity exists for manufacturing to target pulp and end-use paper specifications. However, for process control to succeed, producers must measure continuously and react quickly. Unrecognized variation in a pulp stream wastes resources by yielding a product that fails to meet specifications, while continuous process monitoring can ensure optimum output quality, certified by a contemporaneous record. Here we propose research that will bring new, laser-spectroscopic analytical tools to the production line. Our approach will combine miniaturized instrumentation, focussed sampling protocols, refined multivariate calibration and integrated analysis to enable real-time process control targeted to physical and mechanical properties of fibre network products. Template oriented genetic algorithm (TOGA) feature selection will link chemical markers in pulp-stream Raman spectra to monitor conformance with such end-use mechanical specifications as tensile strength, burst, tear, and pulp viscosity. TOGA features will signal chemical changes that correlate with biometric parameters, calling for process remedies and/or end-product reallocation. Off-line measurements will fuse chemical information with sub-micron spatial measures of fibre system morphology to increase fundamental understanding of network strength and softness. Finally, field research will apply these same chemical and morphological classification tools to map the progress of in-use electronic-materials fibre systems to failure.************************************

来自加拿大北方软木的漂白硫酸盐浆在纤维长度、强度、细度和均匀性方面处于世界领先地位。 然而，造纸技术的进步缩小了NBSK纸浆的优势，增加了加拿大供应商的性价比压力。 为了保持竞争力，加拿大纸浆工业必须创新，开发新的工艺和过程控制策略，以生产更高质量和更低成本的纸浆，减少能源使用和对环境的影响。 纸浆的化学和材料特性决定了它所生产的纤维网的物理特性，如果有适当的过程监测和干预，就有机会按照目标纸浆和最终用途纸张的规格进行生产。 然而，为了成功进行过程控制，生产商必须连续测量并快速反应。 纸浆流中未被识别的变化会产生不符合规格的产品，从而浪费资源，而连续的过程监控可以确保最佳的输出质量，并通过同期记录进行认证。 在这里，我们提出的研究，将带来新的，激光光谱分析工具的生产线。 我们的方法将结合联合收割机小型化仪器、集中采样协议、精细的多变量校准和综合分析，以实现针对纤维网络产品的物理和机械性能的实时过程控制。 模板导向遗传算法（TOGA）的特征选择将链接纸浆流拉曼光谱中的化学标记物，以监测与最终使用的机械规格，如抗张强度，爆破，撕裂和纸浆粘度的一致性。 TOGA特征将发出与生物特征参数相关的化学变化信号，要求进行工艺补救和/或最终产品重新分配。 离线测量将融合化学信息与纤维系统形态的亚微米空间测量，以增加对网络强度和柔软度的基本理解。 最后，现场研究将应用这些相同的化学和形态分类工具来绘制使用中的电子材料纤维系统的进展到故障。