Carbonate precipitation control is important in separation processes such as those involving the monoethylene glycol (MEG) regeneration process in natural gas production plants as glycol reduces the solubility of these salts. The use of sensors that allow continuous monitoring of the precipitation of inorganic salts in aqueous solutions contributes to optimizing the parameters commonly found in these processes. This work proposes the study of calcium and strontium carbonate salt precipitation in H2O + MEG mixtures using near-infrared spectroscopy (NIR) as a monitoring technique. Precipitated salts were obtained from the mixture of solutions at different ionic concentrations (anions and cations), in the absence and presence of MEG (0 or 40 wt %) at 60 °C. The information obtained by the NIR technique was correlated with the data provided by the focused beam reflectance measurement (FBRM) technique, and an algorithm combining principal component analysis (PCA) and artificial neural networks (ANN) was employed to describe the precipitation kinetics of carbonates in the solutions. The results showed that increasing the ionic strength and MEG concentration favors the reduction of the number and size of the calcium carbonate crystals. For solutions containing MEG, the kinetics of crystal growth are reduced. The addition of NaCl increases the ionic strength of the system and affects ion complexation, resulting in a decreased particle size distribution and reduced particle formation. The PCA-ANN model effectively described salt particle growth and precipitation kinetics, demonstrating strong correlations (above 0.90) and low error rates (0.55 for the growth kinetic model and 100 particles for the formation kinetic model), accurately predicting particle formation and growth dynamics. The proposed methodology for the study and monitoring of salt precipitation using NIR techniques proved to be efficient in determining the amount and size of precipitated particles in solutions containing water and MEG under different experimental conditions. This methodology contributes to efficient management by monitoring and controlling the parameters involved in the precipitation and deposition of existing salts in petroleum exploration and production systems.

中文翻译：

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使用 NIR 技术监测 H2O + MEG 混合物中碳酸钙和碳酸锶的沉淀


碳酸盐沉淀控制在分离过程中非常重要，例如涉及天然气生产厂中单甘醇 （MEG） 再生过程的分离过程，因为乙二醇会降低这些盐的溶解度。使用允许连续监测水溶液中无机盐沉淀的传感器有助于优化这些过程中常见的参数。这项工作提出了使用近红外光谱 （NIR） 作为监测技术研究 H2O + MEG 混合物中碳酸钙和碳酸锶盐沉淀的情况。在 60 °C 下不存在 MEG（0 或 40 wt %）的情况下，从不同离子浓度（阴离子和阳离子）的溶液混合物中获得沉淀盐。 将 NIR 技术获得的信息与聚焦光束反射测量 （FBRM） 技术提供的数据相关联，并采用主成分分析 （PCA） 和人工神经网络 （ANN） 相结合的算法来描述碳酸盐在溶液中的沉淀动力学。结果表明，增加离子强度和 MEG 浓度有利于减少碳酸钙晶体的数量和大小。对于含有 MEG 的溶液，晶体生长的动力学会降低。NaCl 的添加增加了系统的离子强度并影响离子络合，导致粒度分布减小和颗粒形成减少。PCA-ANN 模型有效地描述了盐颗粒生长和沉淀动力学，表现出很强的相关性（高于 0.90）和低错误率 （0.生长动力学模型为 55 个粒子，形成动力学模型为 100 个粒子），准确预测粒子形成和生长动力学。事实证明，在不同实验条件下，使用 NIR 技术研究和监测盐沉淀的方法可有效确定含水和 MEG 的溶液中沉淀颗粒的数量和大小。该方法通过监测和控制石油勘探和生产系统中现有盐的沉淀和沉积所涉及的参数，有助于有效管理。