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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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An Artificial Intelligence Platform for Asset Management Contributes to Better Decision-making Tools for Operations,

D Sen¹, A Fashokun, R Angelotti

¹Virginia Tech, Occoquan Lab, 9408 Prince William Street, Manassas, VA, USA.

Water Environment Research : a Research Publication of the Water Environment Federation

|January 6, 2018

Summary

This summary is machine-generated.

An Artificial Intelligence (AI) system optimizes water, wastewater, and reuse plant management by integrating data for improved operations and maintenance. This AI enhances asset management and provides predictive insights for optimal plant performance.

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Area of Science:

Environmental Engineering
Computer Science
Operations Research

Background:

Effective management of water, wastewater, and reuse plants is crucial for resource sustainability and public health.
Current plant management systems often struggle with fragmented data and lack intelligent decision-support capabilities.
Optimizing operations, maintenance, and asset management requires integrated data and advanced analytical tools.

Purpose of the Study:

To develop and implement an Artificial Intelligence (AI) system for enhancing the management of water, wastewater, and reuse plants.
To create an integrated data repository and intelligent layer for improved operational insights.
To enable simulation and optimization of plant processes and asset management strategies.

Main Methods:

Developed an AI system integrating data from diverse computer systems and files.
Implemented an IViewOps (Intelligent View of Operations) model for process and asset simulation.
Utilized AI to analyze current and future operational conditions for predictive management.
Generated output files to inform staff and feed other systems with optimal solutions.

Main Results:

The AI system successfully integrated disparate data sources, creating a unified operational view.
Simulations using the IViewOps model identified optimization opportunities for operations and maintenance.
The system provided actionable insights for staff, leading to informed decision-making.
Enhanced asset and investment management planning through intelligent data analysis.

Conclusions:

The developed AI system significantly improves the management of water, wastewater, and reuse plants.
Integrated data and intelligent analysis provide a robust framework for optimizing plant operations and maintenance.
The IViewOps model offers a valuable tool for simulating scenarios and achieving optimal asset management.