Microbial Data Prediction Rule Generation for Wastewater
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Solution Overview
Problem
Current prediction methods for water quality after wastewater treatment in industrial settings, particularly in chemical and steel industries, fail to accurately consider the states of microbial flora in activated sludge, leading to insufficient prediction accuracy, especially when dealing with diverse wastewaters.
Innovation Solution
A prediction-rule generating system that incorporates time series data of microorganism abundance proportions or nucleotide sequences, combined with water quality information, using principal component analysis to generate prediction rules that account for microbial flora states, allowing for more accurate water quality prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If prediction is performed using only time series data of water quality and operating parameters of biological reaction tank, then the prediction method is simple, but prediction accuracy is insufficient because states of microbial flora in activated sludge are not considered
Solution Approach 1:
The patent segments the complex prediction problem into two parts: first performing principal component analysis on microorganism abundance data to extract key microbial indicators, then using these extracted features alongside traditional water quality parameters for prediction. This segmentation reduces the complexity of directly using all microorganism data while improving prediction accuracy by focusing on the most relevant microbial factors.
Solution Approach 2:
The patent introduces principal component analysis as an intermediary processing step between raw microorganism abundance data and the prediction model. This intermediary transforms high-dimensional microbial data into a manageable set of principal components that capture the essential variation in microbial flora states, enabling accurate prediction without directly handling the complexity of complete microbial community data.
2Adaptability or versatility
If various wastewaters are treated, then the system has high adaptability, but prediction becomes more difficult due to the diversity of wastewater compositions
Solution Approach 1:
The patent creates a universal prediction model that works across different wastewater types by using principal component analysis to extract common microbial patterns that are relevant regardless of wastewater composition. The model is trained on diverse wastewater data and can adapt to predict water quality for various wastewater types using the same microbial indicators and prediction framework.
Solution Approach 2:
The patent changes the prediction approach from using specific wastewater composition parameters to using microbial abundance parameters that remain relevant across different wastewater types. By focusing on how microbial communities respond to and process different wastewaters, the system maintains prediction accuracy across diverse wastewater compositions without needing separate models for each type.
Data Source
AI summary
A computer of a prediction-rule generating system includes an input unit configured to input time series data of an abundance proportion of microorganisms or nucleotide sequences included in activated sludge in which a water treatment is performed and water quality information indicating water quality after a water treatment associated with data at each time constituting the time series data, a principal component analyzing unit configured to perform principal component analysis on the input time series data and calculate principal component scores of data at each time constituting time series data, and a prediction rule generating unit configured to generate a prediction rule for predicting water quality after a water treatment from an abundance proportion of microorganisms or nucleotide sequences on the basis of the calculated principal component scores and the input water quality information indicating water quality after a water treatment.