The development of smart sensors capable to analyse data gathered on the process line and to give a real time feedback has been undergoing extensive research in the last years due to its potential benefits on the process optimisation and products improvement. In this paper, a novel approach to detect and monitor pH and conductivity using 2D electrical resistance tomography (ERT) is proposed for the first time in a reacting system. As a study case, the reaction between phosphoric acid and potassium hydroxide in mediums of both water and sodium carboxymethylcellulose (CMC) aqueous solution was assessed. The information gathered using the ERT have been used to determine local and overall mixing time for a sequence of injections of base and acid to understand the overall performance of the system. In addition, the same information have been used to extrapolate live data about the variation of the pH coupling the ERT data and machine learning techniques. Three different approaches have been investigated to achieve the aforementioned objective all integrating ML to the data processing. The first two approaches did not provide satisfying results showing the limitation of a completely blind approach (pure statistical approaches). However, the last approach, which combined ML technique and physical/chemical knowledge, showed very successful results for the real time monitoring of the pH in a reacting system.

近年来，由于智能传感器在工艺优化和产品改进方面具有潜在的优势，因此能够进行分析的智能传感器的开发已经得到了广泛的研究。在本文中，首次提出了在反应系统中使用二维电阻层析成像（ERT）检测和监测pH和电导率的新方法。作为研究案例，评估了磷酸和氢氧化钾在水和羧甲基纤维素钠（CMC）水溶液中的反应。使用ERT收集的信息已用于确定一系列碱和酸注入序列的局部和总体混合时间，以了解系统的总体性能。此外，相同的信息已用于推断有关ERT数据和机器学习技术的pH值变化的实时数据。已经研究了三种不同的方法来实现上述目标，它们都将ML集成到了数据处理中。前两种方法不能提供令人满意的结果，表明完全盲法（纯统计方法）的局限性。但是，将ML技术和物理/化学知识相结合的最后一种方法显示了非常成功的结果，用于实时监测反应系统中的pH。前两种方法不能提供令人满意的结果，表明完全盲法（纯统计方法）的局限性。但是，将ML技术和物理/化学知识相结合的最后一种方法显示了非常成功的结果，用于实时监测反应系统中的pH。前两种方法不能提供令人满意的结果，表明完全盲法（纯统计方法）的局限性。但是，将ML技术和物理/化学知识相结合的最后一种方法显示了非常成功的结果，用于实时监测反应系统中的pH。