MétaCan
Menu
Back to cohort
Record W2292915668 · doi:10.14288/1.0050500

The use of oxidation-reduction potential (orp) as a process a process control parameter in wastewater treatment systems

2008· article· en· W2292915668 on OpenAlexaff
David G. Wareham

Bibliographic record

VenuecIRcle (University of British Columbia) · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality Monitoring and Analysis
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProcess (computing)Reduction potentialReduction (mathematics)WastewaterProcess controlProcess engineeringOxidation processControl (management)Environmental scienceComputer scienceBiochemical engineeringChemistryPulp and paper industryEnvironmental engineeringEngineeringMathematicsArtificial intelligenceInorganic chemistry

Abstract

fetched live from OpenAlex

This research explored the use of Oxidation-Reduction Potential to control two lab-scale sequencing batch reactor (SBR) wastewater treatment processes. The treatment schemes investigated were the aerobic-anoxic digestion of activated sludge (AASD) and the excess biological phosphorus (Bio-P) removal process. Evaluation of each process consisted of a consideration of the reactor performances coupled with the control stability achieved using two different operating strategies. The first strategy was known as "Fixed-Time Control" (FT), since it represents the "classical" management approach; control is based on conditions externally "fixed" by an operator. For the AASD set of experiments, the "fixed" variable was the ratio of air-on to air-off (3 hours each). For the Bio-P experiments, it was the time of addition of acetate to the reactor (1 hour 25 minutes into the non-aerated sequence). The second strategy was known as "Real-Time Control" (RT), since it represents an optimization technique whereby control conditions are continuously evaluated as time progresses. The Real-Time aspect of control is derived from the fact that ORP measurements evaluate the reactor conditions on-line, by invoking a bacterial vision of the process scheme. For the AASD experiments, this evaluation took the form of proportioning the ratio of air-on to air-off, based upon the bacterial "need" for sufficient time to reduce the nitrates completely to nitrogen gas (denitrification). Sufficient time is determined by the distinctive breakpoint (correlated to nitrate disappearance) occurring in the ORP-time profile. The first experiment (AASD#1) , therefore, had an air-on/airoff ratio of 3 hours air-on/nitrate-breakpoint-determined airoff. The second experiment (AASD#2) had the length of aeration time determined by a match to the previous length of time for denitrification, as determined by the breakpoint. In the Bio-P experiments, the ORP breakpoint was used to "trigger" the addition of acetate to the reactor, thus ensuring the maximum amount of carbon was available for storage by Bio-P organisms. Comparisons between the two reactors revealed that for the AASD strategies, the Real-Time reactor had essentially the same solids degradation as the Fixed-Time reactor (14% - 21%), depending upon the strategy considered, the type of solids (TSS or VSS) and the method of mass balancing used. The RT reactor was observed to obtain marginally better nitrogen removal (up to 6 % in some cases) over the FT reactor. Evaluation of the ORP parameter as a "response indicator", by subjecting the AASD reactors to unsteady process input conditions, revealed that the Real-Time reactor more readily accommodated disturbances to the system. Neither reactor in the Bio-P experiment was particularly successful in consistently removing phosphorus. A potentially useful screening protocol was developed for evaluating reactor performances, based upon the time-of-occurrence of the nitrate breakpoint, assessed against whether it hindered or aided the purpose of acetate addition to a Bio-P SBR.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.299
Threshold uncertainty score0.770

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 head0.019
GPT teacher head0.195
Teacher spread0.176 · 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 designObservational
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

Citations2
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicWater Quality Monitoring and AnalysisFrench-language works237,207