The characteristics of fecal sludge delivered to treatment plants are highly variable. Adapting treatment process operations accordingly is challenging due to a lack of analytical capacity for characterization and monitoring at many treatment plants. Cost-efficient and simple field measurements such as photographs and probe readings could be proxies for process control parameters that normally require laboratory analysis. To investigate this, we evaluated questionnaire data, expert assessments, and simple analytical measurements for fecal sludge collected from 421 onsite containments. This data served as inputs to models of varying complexity. Random forest and linear regression models were able to predict physical-chemical characteristics including total solids (TS) and ammonium (NH₄⁺-N) concentrations, and solid-liquid separation performance including settling efficiency and filtration time (R² from 0.51-0.66) based on image analysis of photographs (sludge color, supernatant color, and texture) and probe readings (conductivity (EC) and pH). Supernatant color was the best predictor of settling efficiency and filtration time, EC was the best predictor of NH₄⁺-N, and texture was the best predictor of TS. Predictive models have the potential to be applied for real-time monitoring and process control if a database of measurements is developed and models are validated in other cities. Simple decision tree models based on the single classifier of containment type can also be used to make predictions about citywide planning, where a lower degree of accuracy is required.

送至处理厂的粪便污泥的特性变化很大。由于许多处理厂缺乏表征和监测的分析能力，因此相应地调整处理过程的操作具有挑战性。具有成本效益的简单现场测量（例如照片和探头读数）可能是通常需要实验室分析的过程控制参数的代理。为了对此进行调查，我们评估了问卷数据，专家评估以及从421个现场收容所收集的粪便污泥的简单分析测量结果。该数据用作不同复杂性模型的输入。随机森林和线性回归模型能够预测物理化学特征，包括总固体（TS）和铵（NH ₄ ⁺-N）浓度，以及固液分离性能，包括沉降效率和过滤时间（R ²从0.51-0.66起），基于照片的图像分析（污泥颜色，上清液颜色和质地）和探针读数（电导率（EC）和pH）。上清液颜色是沉降效率和过滤时间的最佳预测指标，EC是NH ₄ ⁺的最佳预测指标-N，质地是TS的最佳预测指标。如果开发了测量数据库并在其他城市进行了验证，则预测模型有可能应用于实时监控和过程控制。基于容器类型的单个分类器的简单决策树模型也可以用于对需要较低准确性的城市范围内的计划进行预测。