A gas containing approximately 1000 ppm of methyl mercaptan (CH 3 SH) was used to test an oxidative reaction system for the purification of gas. Experiments were performed for 3.0 h periods in a fixed-bed reactor containing 0.25-5.0 g of Hydrodarco activated carbon in the temperature and pressure ranges 323-448 K and 122-364 kPa, respectively. The gas hourly space velocity was varied from 938 to 4000 h -1 , with the O 2 /CH 3 SH ratio varying from 1.1 to 1.33 times the stoichiometric ratio. Dimethyl disulfide was the main product, while CO 2 was produced in small amounts. At temperatures above 373 K, 99.99% conversion of the mercaptan was achieved. It was established that higher conversion of CH 3 SH could be achieved while keeping CO 2 production to a minimum by using an O 2 /CH 3 SH ratio in the feed gas close to 1.10 times the stoichiometric ratio. Catalyst deactivation occurred due to deposition of dimethyl disulfide on the catalyst. A kinetic study of this process was performed, and a rate equation for the conversion of CH 3 SH to (CH 3 ) 2 S 2 and H 2 O was obtained. Since catalyst deactivation occurred by fouling due to deposition of (CH 3 ) 2 S 2 on the catalyst, the initial rates were considered to be global rates without deactivation effects. According to the Langmuir-Hinshelwood model, the overall rate equation was derived on the basis of the mechanism where the rate-determining step is a surface reaction. The rate data obtained using granular activated carbon were collected well with the rate equation.

Low concentrations (e.g. < 3 %) of H,S in natural gas can be selectively oxidized over an "granular Hydrodarco" activated carbon catalyst to elemental sulphur, water and a small fraction of by-product sulphur dioxide, SO,. To optimize the H,S catalytic oxidation process, the process was conducted in the temperature range 125 -200 OC, at pressures 230 -3200 kPa, with the O/H,S ratio being varied from 1.05 to 1.20 and using different types of sour and acid gases as feed. The optimum temperature was determined to be approximately 175 "C for high H,S conversion and low SO, production with an O/H,S ratio 1.05 times the stoichiometric ratio. The life of the activated carbon catalyst has been extended by removing heavy hydrocarbons from the feed gas. The process has been performed at elevated pressures to increase H,S conversion, to maintain it for a longer period and to minimize SO, production. The process is not impeded by water vapour up to 10 mol% in the feed gas containing low concentrations of CO, (< 1.0 %). A decrease in H,S conversion and an increase in SO, production were obtained with an increase in water vapour in the feed gas containing a high percentage of CO,. The process works well with "sour natural gas" containing approximately 1% H,S and with "acid gas" containing both H,S and CO,. It gives somewhat higher H,S conversion and low SO, production with feed gas containing low concentrations of CO, . A kinetics study to determine the rate-controlling step for the H,S catalytic oxidation reaction over "granular Hydrodarco" activated carbon has been conducted. It was concluded that either adsorption of 0, or H,S from the bulk phase onto the catalyst surface is the rate-controlling step of the H2S catalytic oxidation reaction.De faibles concentrations (p.ex. < 3%) de H,S dans du gaz naturel peuvent btre oxydees de maniere selective sur un catalyseur de charbon active "Hydrodarco granulaire", en soufre elementaire, en eau et en une petite fraction de dioxyde de soufre, SO,. Afin d'aoptiminser le procede d'oxydation catalytique du H,S, I'operation a ete conduite a des temperatures comprises entre 125 et 200"C, a des pressions de 230 a 320 kPa, en faisant varier le rapport O/H,S de 1,05 a 1,20 et en utilisant differents types de gaz acides comme alimentation. On a determine que la temperature optimale etait approximativement de 175°C pour une conversion de H,S elevee et une faible production de SO, avec un rapport O/H,S de 1,05 fois le rapport stoechiometrique. La duree de vie du catalyseur de charbon active a ete prolongee en retirant les hydrocarbures lourds du gaz d'alimentation. Le procede a ete mene a pressions elevees pour accroitre la conversion de H,S et, egalement, pour le maintenir plus longtemps et minimiser la production de SO,. Le procede n'est pas entrave par la vapeur d'eau jusqu'a 10% en moles dans le gaz d'alimentation contenant de faibles concentrations de CO, (< 1 ,O%). Une diminution de la conversion du H,S et une augmentation de la production de SO, sont obtenues avec une augmentation de la v...

This research aims to develop a knowledge base for a disaster management question-answering dialogue system. The rapid growth of the amount of data has led to the variance of data in terms of their formats, sources, and attributes. Hence, the difficulties of decision makers to accomplish their missions accurately and efficiently have increased. To solve this problem, we developed a questionanswering dialogue system for disaster management. In our previous research, we found that the information most likely retrieved in response to a user's request can be determined by calculating the similarity between the keywords and the user's input in a handcrafted keyword-information mapping table. However, we also noticed that managing the mapping table was a tedious task. Moreover, for the inputs that had more than one keyword, the system was unable to provide integrated information. Therefore, we constructed a knowledge base to optimize the performance and maintainability of the system. To build the knowledge base for disaster management, we designed the domain model by performing an abstraction on the knowledge of professional information providers and the required data on disaster management, while considering their source, certainty, and spatiotemporal features. The query of requested information from the knowledge base is composed of mentioned entities in the user's input. For the dialogue system to recognize the entities, we applied entity recognition. The subtasks include segmentation, tagging, similarity calculation with the names of the entities in the knowledge base, and intent detection to determine the desired knowledge of the user.

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