Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings
The residual lifetime of an activated carbon filter bed challenged by hydrogen cyanide, a chemisorbing gas, has been successfully predicted on one occasion using transducers embedded within the filter bed. No thermostatting nor relative humidity control of the bed or influent challenge was imposed....
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| Format: | Article |
| Language: | English |
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SAGE Publishing
1999-10-01
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| Series: | Adsorption Science & Technology |
| Online Access: | https://doi.org/10.1177/026361749901700907 |
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| _version_ | 1841564595926335488 |
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| author | P.J.C. Anstice J.F. Alder |
| author_facet | P.J.C. Anstice J.F. Alder |
| author_sort | P.J.C. Anstice |
| collection | DOAJ |
| description | The residual lifetime of an activated carbon filter bed challenged by hydrogen cyanide, a chemisorbing gas, has been successfully predicted on one occasion using transducers embedded within the filter bed. No thermostatting nor relative humidity control of the bed or influent challenge was imposed. Four thermocouples and a fibre optic pH probe, sensitised to HCN by the addition of cobalt(II) chloride, were used to measure the progress along the filter bed of the adsorption front resulting from intermittent HCN challenges of 0.9 mg/dm 3 . Twenty randomised neural networks for cyanogen breakthrough prediction were created. These were based on a simple system, with the occurrences of maximum temperatures around two thermocouples at the front of the filter bed providing the input variables and the cyanogen breakthrough time (BT) being the response variable. Temperature maxima were thought to be due to cyanogen hydrolysis. Each network was trained with the data from four completed challenges and then interrogated with the input variables from a fifth. The mean response variable produced was 36.5 d (± 2.1 d standard deviation). This represented 94% of the actual observed time to breakthrough which occurred on challenge day 39. Averaging of network responses was required because four training examples only loosely map the possible variable space such that the potential error in any one network prediction is large. This preliminary set of experiments is most encouraging, having successfully predicted the lifetime of the bed whilst it was only half exhausted. The fact that, with no climatic control, simple sensors still permitted this achievement under realistic conditions of intermittent, long-term exposure illustrates the potential of this approach to residual lifetime monitoring. Further studies to repeat this success and extend the work to other challenge gases are now required. |
| format | Article |
| id | doaj-art-e1af14d58d1340a1b612eec02cf4a7ee |
| institution | Kabale University |
| issn | 0263-6174 2048-4038 |
| language | English |
| publishDate | 1999-10-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Adsorption Science & Technology |
| spelling | doaj-art-e1af14d58d1340a1b612eec02cf4a7ee2025-01-02T22:37:24ZengSAGE PublishingAdsorption Science & Technology0263-61742048-40381999-10-011710.1177/026361749901700907Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary FindingsP.J.C. AnsticeJ.F. AlderThe residual lifetime of an activated carbon filter bed challenged by hydrogen cyanide, a chemisorbing gas, has been successfully predicted on one occasion using transducers embedded within the filter bed. No thermostatting nor relative humidity control of the bed or influent challenge was imposed. Four thermocouples and a fibre optic pH probe, sensitised to HCN by the addition of cobalt(II) chloride, were used to measure the progress along the filter bed of the adsorption front resulting from intermittent HCN challenges of 0.9 mg/dm 3 . Twenty randomised neural networks for cyanogen breakthrough prediction were created. These were based on a simple system, with the occurrences of maximum temperatures around two thermocouples at the front of the filter bed providing the input variables and the cyanogen breakthrough time (BT) being the response variable. Temperature maxima were thought to be due to cyanogen hydrolysis. Each network was trained with the data from four completed challenges and then interrogated with the input variables from a fifth. The mean response variable produced was 36.5 d (± 2.1 d standard deviation). This represented 94% of the actual observed time to breakthrough which occurred on challenge day 39. Averaging of network responses was required because four training examples only loosely map the possible variable space such that the potential error in any one network prediction is large. This preliminary set of experiments is most encouraging, having successfully predicted the lifetime of the bed whilst it was only half exhausted. The fact that, with no climatic control, simple sensors still permitted this achievement under realistic conditions of intermittent, long-term exposure illustrates the potential of this approach to residual lifetime monitoring. Further studies to repeat this success and extend the work to other challenge gases are now required.https://doi.org/10.1177/026361749901700907 |
| spellingShingle | P.J.C. Anstice J.F. Alder Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings Adsorption Science & Technology |
| title | Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings |
| title_full | Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings |
| title_fullStr | Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings |
| title_full_unstemmed | Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings |
| title_short | Towards the Prediction of the Remaining Lifetime of a Carbon Filter Bed on Hydrogen Cyanide Challenge Using in-Bed Sensors and a Neural Network: Preliminary Findings |
| title_sort | towards the prediction of the remaining lifetime of a carbon filter bed on hydrogen cyanide challenge using in bed sensors and a neural network preliminary findings |
| url | https://doi.org/10.1177/026361749901700907 |
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