Abstract
Current energy production chains are carbon-based or derived from a carbon source (i.e., oil and gas), which have negative impacts on environment because of CO2 emission and other greenhouse gases. Growing world energy demand from fossil fuels plays a key role in the upward trend in CO2 emissions. Petrochemical and chemicals industries among other industrial sectors have much devastative influences on CO2 emissions through producing organic and inorganic products embodied carbon such as olefins, aromatics, ammonia, and carbon black or oil and gas combustions. The Organization of the Petroleum Exporting Countries (OPEC) states that the average portion of CO2 emissions from oil and gas usage is expected to double by 2050 (OPEC 2011). With current available technologies, the options for replacing fossil fuels or switching to less carbon fossil fuels are limited. These fossil fuels will likely remain to be the predominant source of energy in industry at least for this century. However, increasing costs for waste disposal and emissions control, growing international regulatory pressure, and increasing public demands for environmental quality are forcing nations to lay foundations for global agreement to curb CO2 emissions, for example the UN climate negotiations in Qatar, Kyoto Protocol, and OECD. To mitigate or eliminate adverse environmental impacts due to specific products and processes, national efforts have been conducted in order to switch to more efficient technologies, and to life cycle and system optimization approaches (DOE 2009). The Waste and Resources Action Program (WRAP) highlights the importance of product life cycles and operation management system’s optimization as two complementary approaches for achieving sustainable production process in the USA, the UK, the EU, and Japan (Brown et al. 2012). These two approaches attempt to reach optimal resource efficiency and sufficiency through waste reduction, lean production, industrial synergies, extended product lifetime, efficient use of equipment, and equipment lifetime optimization. For example, British Petroleum has recently launched the sustainability management system’s project in order to enhance HSE mitigation, and thus to earn back trust following the Gulf of Mexico accident in 2011 (BP 2011). Japan Society for the promotion of Science (JSPS) allocated research fellowships for sustainability engineering for equipment efficiency and lifetime optimization in the wake of Fukushima nuclear accident in 2011 (JSPS 2012). McKinsey & Company reported that according to Dow Chemical and Corning’s experience, low-carbon economy and energy efficiency are the new challenge of heavy industry companies in North America (McKinsey 2011). The Energy Academy Europe (EAE) has run a number of projects with the main themes of carbon capturing application and resource efficiency (EAE 2012).
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Notes
- 1.
RDF is a data model for objects (“resources”) and relations between them. It provides a simple semantics for data model, and these data models can be represented in XML syntax. A formal data is a model from the W3C using XML for the description of web resources using machine readable metadata. It has potential for use in the semantic web.
Abbreviations
- CBM:
-
Condition based maintenance
- CMMS:
-
Computerized maintenance system
- DT:
-
Destructive test
- ETA:
-
Event tree analysis
- FMEA:
-
Failure mode event analysis
- FTA:
-
Fault tree analysis
- HAZOP:
-
Hazard and operability study
- HHR:
-
Human health risk
- HSE:
-
Health safety environment
- LAD:
-
Logical analysis of data
- LCC:
-
Life cycle cost
- NDT:
-
Non-destructive test
- OREDA:
-
Offshore reliability data handbook
- OSHA:
-
Occupational safety and health administration
- OWL:
-
Ontology Web language
- RBI:
-
Risk-based inspection
- RCM:
-
Reliability-centered maintenance
- RDF:
-
Resource description framework
- STEP:
-
Standard for the exchange of plant
- W3C:
-
World Wide Web Consortium
- XML:
-
Extensible Markup Language
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Ebrahimipour, V., Yacout, S. (2015). Ontology-Based Knowledge Platform to Support Equipment Health in Plant Operations. In: Ebrahimipour, V., Yacout, S. (eds) Ontology Modeling in Physical Asset Integrity Management. Springer, Cham. https://doi.org/10.1007/978-3-319-15326-1_8
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