Raw data for the manuscript "MLSens Settler: Development and Evaluation of a Novel Self-Cleaning Approach for Horizontal Tube Settlers

This dataset contains the raw experimental data and theoretical upscaling analyses supporting the findings of the manuscript "MLSens Settler: Development and Evaluation of a Novel Self-Cleaning Approach for Horizontal Tube Settlers".

Research Hypothesis and Context: The research addresses the historical limitation of horizontal tube settlers, which are theoretically more efficient but were abandoned due to a lack of a continuous self-cleaning mechanism. The central hypothesis is that incorporating a longitudinal bottom slot in each horizontal tube can solve this drawback, enabling effective self-cleaning without compromising sedimentation performance.

Data Collection: The data was gathered from a custom-built, pilot-scale MLSens settler treating synthetic raw water under controlled laboratory conditions. Key performance parameters, including turbidity, apparent color, pH, and temperature, were systematically monitored at the inlet and outlet of the settler during 10 independent experimental runs (n=130 total measurements).

Data Findings and Interpretation: The data presented here validates the success of the MLSens concept. The experimental data demonstrates the settler's high performance, showing consistent turbidity removal efficiencies exceeding 95% and a stable effluent quality (< 4 NTU), suitable for subsequent filtration stages. The upscaling analysis data provides the calculations that show the design's potential to achieve high surface overflow rates (13–21 m/h) and significantly reduce the footprint (~51%) of sedimentation units compared to conventional inclined settlers.

File Structure: The dataset is organized into two main spreadsheets:

Experimental_Raw_Data.xlsx: Contains all data related to the pilot-scale experiments, including raw time-series measurements, summary statistics, and a simple upscaling scenario based on the pilot unit.

Upscaling_Analysis_Data.xlsx: Contains the detailed theoretical upscaling and parametric sensitivity analyses for a full-scale design.

Please refer to the README.txt file for a detailed description of the contents of each file and their respective sheets.

本数据集包含支撑学术论文《MLSens Settler：一种新型水平管式沉淀池自清洁方法的开发与评估》研究结论的原始实验数据与理论放大分析内容。

研究假设与背景：本研究针对水平管式沉淀池的历史局限性展开——该类沉淀池理论上效率更优，但因缺乏连续自清洁机制而被弃用。本研究的核心假设为：在每根水平管的底部开设纵向槽口，可解决这一缺陷，在不影响沉淀性能的前提下实现高效自清洁。

数据采集：数据来自定制化中试规模MLSens沉淀池（MLSens Settler），在可控实验室条件下处理合成原水。在10组独立实验运行（总测量样本量n=130）期间，系统监测了沉淀池进、出水口的浊度（NTU）、表观色度、pH值与温度等关键性能参数。

数据发现与解读：本数据集所呈现的实验数据验证了MLSens概念的可行性。实验结果表明该沉淀池性能优异，浊度去除率稳定维持在95%以上，出水水质稳定（浊度<4 NTU），可适配后续过滤工序。放大分析数据则提供了相关计算结果，证实该设计可实现较高的表面溢流负荷（13–21 m/h），与传统斜管沉淀池相比，可大幅缩减沉淀单元占地面积约51%。

文件结构：本数据集包含两个主要电子表格：
1. Experimental_Raw_Data.xlsx：收录所有中试实验相关数据，包括原始时序测量数据、汇总统计量，以及基于中试装置的简易放大分析场景。
2. Upscaling_Analysis_Data.xlsx：收录针对全规模设计的详细理论放大分析与参数敏感性分析内容。

请参阅README.txt文件，以了解各文件及其内部工作表的详细内容说明。