Method and system for targeting and monitoring the energy performance of manufacturing facilities

• US 7,103,452 B2
• Filed: 12/23/2004
• Issued: 09/05/2006
• Est. Priority Date: 12/29/2003
• Status: Expired due to Fees

• Current Assignee: GranBio Intellectual Property Holdings, LLC
• Original Assignee: Theodora Retsina
• Inventors: Retsina, Theodora
• Primary Examiner(s): Von Buhr, Maria N.
• Application Number:
  US10/905,290
• Publication Number:
  US 20050143953A1
• Time in Patent Office: 621 Days
• Field of Search: 700/95, 700/96, 700/108, 700/174, 700/291, 702182-186
• US Class Current: 700/291
• CPC Class Codes: G05B 23/0294 Optimizing process, e.g. process efficiency, product quality

Abstract

An on-line neural network based software application that enables manufacturing facilities to meaningfully determine their energy performance, no matter how complex, with respect to the production rates and ambient conditions. This is achieved by the generation of three levels of targets; facility overall performance, departmental key performance indicators, and key operating parameters that impact the facility'"'"'s energy consumption and over which, the operators have control. A unit and cost gap analysis of actual versus target is executed for overall and departmental energy performance. Causes of statistically significant deviations are diagnosed and corrective actions highlighted. The software application is designed to be updated dynamically so that users can effectively manage performance on the basis of current information.

Claims

1. A method for real-time targeting and monitoring, performing gap analysis, identifying and diagnosing statistically significant deviations, and identifying corrective actions for the energy performance of manufacturing facilities comprising:

utilizing a customizable software application and computerized system to perform the following tasks;

develop a neural network model to generate a target for a manufacturing facility'"'"'s plant wide overall energy using current production rates and ambient conditions, and

develop a single neural network model or linear models to generate targets for departmental standard parameter performance indicators, dynamically adjusted for current production and ambient conditions, and

identify operating parameters that are determined to have the greatest statistical significance on the department standard parameters and over which the facility'"'"'s operators have control through their actions, and

develop and populate a database of the software application; and

enter actual energy usages and costs, department operating rates, and ambient conditions into said software application; and

run the application'"'"'s software to calculate target energy usages and costs, statistical analysis of deviations between actual energy usage and cost and target energy usage and cost, and perform a gap analysis of actual versus target energy usage and cost values; and

diagnose the cause of statistically significant deviations and identify actions to be taken to reduce the deviations; and

display calculated values on performance report of the application; and

store calculated results in the application'"'"'s database; and

electronically export the calculated results and performance report, to production departments, a management information system, or other electronically connected destination and/or print these as hard copy reports.