Abstract: There is provided a method for controlling an RO system, the method being capable of reducing power consumption (that is, CO2) and the amount of waste by reducing the amount of use of chemicals and the number of times of exchanging a membrane and capable of enabling stable operation and contributing to energy saving. A method for controlling an RO system, the RO system including a plurality of RO apparatuses 41 to 44 arranged in parallel and a control unit controlling a start/stop process, the start/stop process including an operation process and a stop process for the RO apparatuses 41 to 44, wherein control is performed so that the number of times of start/stop is larger for an RO apparatus that easily recovers treatability by start/stop.

Abstract: Systems and methods for automated or computer assisted planning of pipe replacement projects includes searching a database of pipe segments each having assigned or calculated failure risk parameters. The searching algorithm looks for connected pipe segments falling within a customer's specified project size and identifies potential projects having a relatively high combined high failure risk.

Abstract: An improved solution accurately predicts of an underground pipe's likelihood of leaking. A data-driven approach uses a combination of information acquisition, classification, regression and/or machine learning. The replacement of underground pipes can be prioritized. Pipe data is inputted and processed. Potential features within the cleaned data is used in pipe life of failure prediction models. The importance of the potential features is ranked. The most important features are extracted and applied to a likelihood of failure model that is created based on historical data and machine learning. Future likelihood of failure for each pipe in the network of pipes can be predicted using the model.

Abstract: Systems and methods for automated or computer assisted planning of pipe replacement projects includes searching a database of pipe segments each having assigned or calculated failure risk parameters. The searching algorithm looks for connected pipe segments falling within a customer's specified project size and identifies potential projects having a relatively high combined high failure risk.

Abstract: Systems and methods calculate consequence of failure values for pipe segments in a network. The calculation can be based on an estimated repair cost, a monetary value associated with the loss of service to customers and also a monetary value associated with the interruption of transportation such as traffic interruption. The calculation can also take into account a pipe segments proximity to a critical facility.

Abstract: A system receives and automatically transforms utility pipe attribute data and pipe break data. The missing and/or incorrect entries in the pipe attributes and/or break data is automatically identified and correct values for these entries are is automatically imputed to generate improved datasets of the pipe attribute data and break data. The improved data can be used to build a model with machine learning. Predictions of future likelihood of failure for pipe sections in a network of pipes can be made based on the model. A national database can be created that is filled with environmental data that has been transformed, optimized, merged, and imputed. The national database can be used for many customers to save computational costs. The national database can be used to build the failure prediction model for utility companies thereby saving computational costs.

Abstract: An improved solution accurately predicts of an underground pipe's likelihood of leaking. A data-driven approach uses a combination of information acquisition, classification, regression and/or machine learning. The replacement of underground pipes can be prioritized. Pipe data is inputted and processed. Potential features within the cleaned data is used in pipe life of failure prediction models. The importance of the potential features is ranked. The most important features are extracted and applied to a likelihood of failure model that is created based on historical data and machine learning. Future likelihood of failure for each pipe in the network of pipes can be predicted using the model.