Zhang Zhipeng, Wicaksana Filicia, An Jingxian, Ma Xiaoyuechuan, Woo Meng Wai, Wei Kegang
Jiangxi Copper Technology Institute Co. Ltd., Nanchang 330096, China.
Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand.
ACS Omega. 2025 Jul 14;10(29):31393-31400. doi: 10.1021/acsomega.5c00357. eCollection 2025 Jul 29.
Phosphorus is essential for all living organisms and is widely used in various industries. However, phosphorus resources are nonrenewable and may soon be depleted. Struvite, a high-quality slow-release compound fertilizer containing nitrogen, phosphorus, and magnesium, is a form of phosphorus recovery product. It is typically recovered by applying magnesium salt chemicals to nitrogen- and phosphorus-rich wastewater. Struvite purity and recovery rate are critical quality parameters that dominate its market price and feasibility as a fertilizer. Traditional methods for determining the struvite purity and recovery rate involve complicated sample preparation and analysis processes, which are labor-intensive and would be adverse to the automation of continuous analysis during large-scale struvite recovery. This study presents a new method for simultaneously determining struvite purity and phosphorus recovery by analyzing optical microscope images and combining saturation index information using principal component analysis via MATLAB scripts. The prediction results of this model closely match those of traditional chemical and instrumental analysis methods, suggesting that a reliable model for struvite product quality can be developed by using only physical properties, including optical crystal images and saturation index information. This advancement provides a theoretical foundation for real-time monitoring of the struvite purity.
磷对所有生物都至关重要，并且广泛应用于各个行业。然而，磷资源是不可再生的，可能很快就会枯竭。鸟粪石是一种含有氮、磷和镁的优质缓释复合肥料，是磷回收产品的一种形式。它通常是通过向富含氮和磷的废水中添加镁盐化学品来回收的。鸟粪石的纯度和回收率是决定其市场价格和作为肥料可行性的关键质量参数。传统的测定鸟粪石纯度和回收率的方法涉及复杂的样品制备和分析过程，这些过程劳动强度大，不利于大规模鸟粪石回收过程中连续分析的自动化。本研究提出了一种新方法，通过分析光学显微镜图像并利用MATLAB脚本结合主成分分析的饱和度指数信息，同时测定鸟粪石纯度和磷回收率。该模型的预测结果与传统化学和仪器分析方法的结果非常吻合，这表明仅使用包括光学晶体图像和饱和度指数信息在内的物理性质就可以开发出可靠的鸟粪石产品质量模型。这一进展为鸟粪石纯度的实时监测提供了理论基础。
