Near-miss Management LLC

Ulku G Oktem

Ankur Pariyani

Brett Emaus

Steven DeLaurentis

Daniel Shumway

Matthew L Dering

Dmitriy Bespalov

Luis Cielak

The invention provides a dynamic risk analyzer (DRA) that periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRA assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.

Dynamic prediction of risk levels for manufacturing operations through leading risk indicators: dynamic exceedance probability method and system

Dynamic prediction of risk levels for manufacturing operations through leading risk indicators: dynamic risk sloping trend method and system

A dynamic risk fault tree (DRFT) for displaying and analyzing risk levels for manufacturing operations. The DRFT incorporates a Dynamic Risk Analyzer (DRA) to periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRFT assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The DRA and hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.

Dynamic prediction of risk levels for manufacturing operations through leading risk indicators: dynamic risk fault tree method and system

The dynamic risk analyzer (DRA) provided by the present invention periodically assesses real-time or historic process data, or both, associated with an operations site, such as a manufacturing, production, or processing facility, including a plant's operations, and identifies hidden near-misses of such operation, when in real time the process data appears otherwise normal. DRA assesses the process data in a manner that enables operating personnel including management at a facility to have a comprehensive understanding of the risk status and changes in both alarm and non-alarm based process variables. The hidden process near-miss data may be analyzed alone or in combination with other process data and/or data resulting from prior near-miss situations to permit strategic action to be taken to reduce or avert the occurrence of adverse incidents or catastrophic failure of a facility operation.

Dynamic prediction of risk levels for manufacturing operations through leading risk indicators: dynamic risk analyzer engine

Provided are methodologies to properly assess and manage operational risks at operations sites, e.g., a manufacturing, production or processing facility, such as a refinery, chemical plant, fluid-catalytic-cracking units, or nuclear energy plant, or a biological or waste management facility, airport or even financial institutions, or at any facility in which operations are often accompanied by risk associated with many high-probability, low-consequence events, often resulting in near-misses. In some operations, processes are monitored by alarms, but the invention operates on either process data or alarm data. The methods are based upon measurement of one or more variables, and/or utilization and management of the concept of 'hidden process near-miss(es)' to identify a change or escalation, if any, in probability of occurrence of an adverse incident. The methodologies combine a plurality of subsets (also useful independently) of dynamically calculated leading risk indicators for dynamic risk management.

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Philadelphia, PA, United States of America

Ulku G Oktem