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Engineering for a small planet

A typical Aspen Fidelis Reliability software model depicts the process plant diagrammatically with representations of raw material inputs e.

Income producing ASSETS that will make you rich in 2019!!

To simulate a modelled system, Aspen Fidelis Reliability uses the Monte Carlo simulation technique, which is based on repeated random sampling to obtain numerical results. This assigns random values to model variables — for example, process downtimes and equipment repair times — based on probability distributions. According to the company, since this approach considers the uncertainty inherent in complex systems, it can provide better solutions than approaches based on deterministic analysis in which fixed values are used for model variables.

Aspen Fidelis Reliability is designed to enable users to get insights into the future performance of the system and analyze and quantify the value of plant improvement opportunities. A particularly powerful feature of Aspen Fidelis Reliability is its flow optimizer, which enables the flow inside the model to dynamically change based on a selected objective function. For example, with revenue as the objective function and feedstock transferable to three different process units, flow is directed first to the highest margin unit, depending upon any constraint conditions. Aspen Fidelis Reliability software can be applied in both the design and operational phases of the plant, which means adopters of the software fall broadly into the two camps of EPCs and owner-operators.

For large capital projects, EPCs must often specify their use of the simulation tool as part of the RFQ, since this would help ensure that facility design decisions would be made based on solid data via tested models, rather than abstract ideas. Once the plant is built and process operations started, the owner-operator can use Aspen Fidelis Reliability to test and validate ongoing improvement efforts, such as new equipment installations, flow redesigns, revised raw material and finished-goods schedules, and modified maintenance strategies.

In one application for a multi-plant integrated site, Dow Chemical wanted to understand the relationship between on-site inventory levels of key intermediates and overall productivity of the complex. Modelling the plant in Aspen Fidelis Reliability involved specifying critical plant assets along with attributes such as failure rates and repair times. The simulation output revealed that, under current operating setup and conditions, there was a slight possibility of another total plant shutdown occurring.

This was not a risk SABIC was willing to take, and it has since reviewed maintenance processes and added operating capacity, based directly on information provided by the AspenTech simulation tool. Predix Asset Performance Management Predix APM is a suite of software and service solutions designed to help optimize the performance of your assets. Predix APM connects disparate data sources and uses advanced analytics to turn data into actionable insights while fostering collaboration and knowledge-management across an organization.

Predix APM gives organizations the flexibility to develop new analytics and applications, making it versatile to meet changing needs. Reduce unplanned downtime and increase availability and reliability by helping to ensure critical assets and systems are monitored and protected from emerging threats.

Facilities Operations & Maintenance - An Overview

Improve workforce productivity by prioritizing maintenance based on criticality and cost condition-based , rather than schedule-based maintenance practices. Protect the health and safety of employees, the environment, and business objectives, by reducing asset-related incidents and unplanned downtime.

Reduce TCO delivered via a Software-as-a-Service SaaS model, providing flexibility and access to business insights where it matters—with edge, cloud, and hybrid configurations. A standardized way to connect machines, data, and people with a consistent interface for superior user experience, dynamic scalability, and extendibility to grow functionality as business needs evolve. APM is at the core of this change. Standardize the collection, integration, modeling, and analysis of disparate data to a single, unified view. This Predix APM solution accelerates time-to-value, leverages machine intelligence, and enables analytics that can detect and diagnose asset performance issues.


Combined with a Risk Reduction analysis to identify the risk reduction of various alternatives considered, the information from Life Cycle Cost preparation is summarized in a business case, providing a consistent approach to the review of projects. The life cycle of an asset is defined as the time interval between the initial planning for the creation of an asset and its final disposal.

This life cycle is characterized by a number of key stages:. As shown in Figure 2, there are day-to-day, periodic and strategic activities that may occur for any asset. The asset life cycle begins with strategic planning, creation of the asset, operations, maintenance, rehabilitation, and on through decommissioning and disposal at the end of the assets life.

The life of an asset will be influenced by its ability to continue to provide a required level of service. Many assets reach the end of their effective life before they become non-functional regulations change, the asset becomes non-economic, the expected level of service increases, capacity requirements exceed design capability. Technological developments and changes in user requirements are key factors impacting the effective life of an asset. A major portion of projected life cycle costs stems from the consequences of decisions made during the early phases of asset planning and conceptual design.

It is the early decisions made during the design of an asset, definition of operations and maintenance requirements, and setting of the operating context of the asset that commit a large percentage of the life cycle costs for that asset. Figure 3 provides an indication of the level of cost reduction that can be achieved at various stages of the project. It shows that as a project moves from strategic planning that the majority of decisions have been made that provide the majority of the cost to the project.

The best opportunities to achieve significant cost reductions in life cycle costs occur during the early concept development and design phase of any project. At this time, significant changes can be made for the least cost. To achieve the maximum benefit available during this stage of the project it is important to explore the following:.

Design for Reliability: Developing Assets that Meet the Needs of Owners

The concept of the life cycle of an asset provides a framework to document and compare alternatives. It is unlikely that all seven of the alternatives listed above are feasible for each analysis; rather than waste money on obviously irrelevant options, the practitioner is encouraged to reduce the analyzed set to only those that are thought to be feasible. A single intervention option for the entire life cycle is not likely to be the best approach to maximizing the life extension for an asset.

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Multiple strategies and options will need to be studied to determine the optimal strategy or combination of strategies for maximum life extension. Optimal Renewal Decision Making uses life cycle cost analysis as a core Tool for determining the optimum intervention strategy and intervention timing.

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Knowing with certainty the exact costs for the entire life cycle of an asset is, of course, not possible; future costs can only be estimated with varying degrees of confidence. Future costs are usually subject to a level of uncertainty that arises from a variety of factors, including:. The main goal in assessing life cycle costs is to generate a reasonable approximation of the costs consistently derived over all feasible alternatives , not to try and achieve a perfect answer. As rehabilitations and or replacement of assets occur during the life cycle, adjust both operations and maintenance costs appropriately.

Both maintenance and operations costs are likely to materially increase as the asset ages. The timing of the rates of increases in the flow of costs over time are instrumental in determining total life cycle costs and can substantially impact the outcome of the investment decision.