Multi-sensor Dean Stark alternative for online bitumen/solids/water estimation

在油砂检测方面，目前商业化的技术很少，传感器的缺乏阻碍了提取过程的自动化、监测、控制和优化。本项目涉及开发一种传感器，可以在线测量油砂提取过程中的组分，即沥青、水和粘土含量。这些信息对于工艺的监测、控制和优化至关重要，但由于许多原因而具有挑战性：工艺流中的成分变化很大，以及混合物本身在组分数量方面的复杂性。本项目将通过使用多功能传感器，即一系列技术，并通过使用复杂的数据融合和信息学技术将来自这些传感器的信息结合起来，来解决这一问题。传感策略的要素包括多种光谱和基于微波的技术，这些技术可以在线部署。这些传感器将针对离线技术进行校准，以便它们可以在“软传感”框架中在线使用。

In the context of oil sands sensing, very few commercial technologies exist, and the lack of sensorshas held back progress in automation, monitoring, control and optimization of the extraction process.This project involves the development of a sensor that can measure the composition, i.e. the bitumen, water and clay content in the process streams in oil sands extraction online. This information is vital for the monitoring, control and optimization of the process, but is challenging for many reasons: the widely varying composition in the process streams and the complexity of the mixtures themselves in terms of the number of components. This will be addressed in this project by using a multifunctional sensor, i.e. an array of techniques, and by combining the information from these sensors using sophisticated data fusion and informatics techniques. The elements of the sensing strategy include multiple spectroscopic and microwave-based techniques, which can be deployed online. These sensors will be calibrated against offline techniques so that they can then be employed online in a 'soft sensing' framework.