Abstract: A fluid vapor distillation system. The system includes a control system for controlling a fluid vapor distillation apparatus including a blow down controller for controlling a blow down valve, a source flow controller for controlling a source flow valve, and a blow down level sensor in communication with a blow down controller and a source flow controller, the blow down level sensor sends signals related to the blow down level to the blow down controller and the source flow controller indicative of the blow down level, wherein the source flow controller actuates the source flow valve based at least on the blow down level sensor signals, and wherein the blow down controller actuates the blow down valve based at least on the blow down level sensor signals, whereby the blow down level and the source flow level are maintained using the blow down level sensor signals as input.

Abstract: A System and method for the application of in-memory analysis of network data applied to logistics best facility recommendations with current market information comprising multiple data extractors, a descriptive module, a predictive module, a learning module, at least one application programming interface, and a visualization tool are disclosed. An example of network data is machine readable data that is acquired through an application programming interface. An example of in-memory analysis is the use of in-memory processing and storage objects. A descriptive module is configured to produce logistics network features. An unsupervised learning module is configured to produce logistics best facility recommendations and a visualization tool is configured to evaluate one or more logistics network scenarios and to display logistics network features with maps and charts.

Abstract: A System and method for the application of in-memory analysis of network data applied to profitability modeling with current market information comprising multiple data extractors, a descriptive module, a predictive module, a learning module, at least one application programming interface, and a visualization tool are disclosed. An example of network data is machine readable data that is acquired through an application programming interface. An example of in-memory analysis is the use of in-memory processing and storage objects. A descriptive module is configured to produce market features. An unsupervised learning module is configured to produce profitability models and a visualization tool is configured to evaluate one or more market scenarios and to display profitability features with maps and charts.

Abstract: A System and method for the application of in-memory analysis of network data applied to site selection with current market information, demand estimates, and competitor information comprising multiple data extractors, a descriptive module, a predictive module, a learning module, at least one application programming interface, and a visualization tool are disclosed. An example of network data is machine readable data that is acquired through an application programming interface. An example of in-memory analysis is the use of in-memory processing and storage objects. A descriptive module is configured to produce market features. An unsupervised learning module is configured to produce site selection market segments and a visualization tool is configured to evaluate one or more market scenarios and to display market features with maps and charts.