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Record W2002287055 · doi:10.2118/2004-050

Cost Effective Sulfur Recovery Solutions For Canada's Greener Environment

2004· article· en· W2002287055 on OpenAlexaboutno aff
T.K. Chow, John Gebur, Vincent Wai‐Sun Wong, C.H. Lawrence

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
Fundersnot available
KeywordsSulfurComputer scienceEnvironmental scienceChemistry

Abstract

fetched live from OpenAlex

Abstract To strive to achieve a greener environment for the well being of Canadian citizens and residents, Canadian environmental regulatory agencies continue to promulgate more stringent standards for sulfur emissions from processing facilities of Oil &Gas Production, Petrochemical and Refining industries. Due to these stringent sulfur emissions regulations, operation reliability of sulfur recovery facility becomes vital to warrant continuous daily overall plant operation. It is therefore important for operators to understand the relevant sulfur issues to facilitate their selection and implementation of proper advanced, reliable and cost effective technologies for sulfur recovery to ensure continuous, reliable and smooth plant operation, thus achieving and maintaining a greener environment. This paper focuses on key advanced technologies for cost effective conversion and recovery of H2S from gas streams to elemental sulfur. Technology and design considerations in handling contaminants contained in the feed gases, in enhancing overall sulfur recovery efficiency and in increasing processing capacity will be addressed in this paper. The following key considerations will be dealt with in detail in the paper.Quality and compositions of acid gases: Cost effective technologies to handle contaminants such ammonia, Benzene, Toluene and Xylene (BTX), and cyanides etc.Sulfur Recovery Efficiency Enhancement: Cost effective technology and optimum process configurations for revamping existing units and installing new units in enhancing sulfur recovery efficiency.Cost effective solutions for expanding processing capacity of existing sulfur plants. To absolutely minimize the sulfur emissions and in the interest of operation personnel safety, increasing activities in recovering H2S and entrained elemental sulfur from vent gases purged from sulfur pits, sulfur storage tanks and sulfur tank car loading/unloading facilities are seen in refining and gas processing facilities. Regulatory requirements for such recovery impose interesting challenges for operators and designers. This paper addresses various technology options for accomplishing the desired sulfur recovery in meeting the regulatory requirements. Pros and cons of these various technology options will be discussed in this paper. This paper also provides an overview of short term and long term economic implications in utilizing and implementing these advanced technologies. Introduction In the wake of the global warming and acid rain issues, environmental regulatory agencies around the globe continue to promulgate more stringent standards for sulfur emissions from processing facilities of Oil &Gas Production, Petrochemical and Refining industries. Sulfur emission regulations govern both single point emission source such as the sulfur recovery plant and a bubble source such as the entire processing facility. These regulations in Canada require new plants to achieve sulfur recovery in the range of 98.5 to 99.7 percent. It is expected that these standards will become more stringent in the future. The Alberta Energy and Utilities Board (EUB) and Alberta Environment (AENV) believe that "sulfur recovery requirements represent minimum expectations and that it is in the public interest for operators of sour gas plants to implement continuous improvement programs to reduce emissions. Particularly in the case of grandfathered sour gas plants, operators are encouraged to take cost-effective measures early to enhance sulfur recovery beyond the minimum requirements discussed in Section 3.1 of Interim Directive ID 2001–3 (italics added)."1 It is advisable to select and implement

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.209
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2004
Admission routes1
Has abstractyes

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