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Abstract An Optimization technique has been introduced to extract the nonideal parameters of gas absorption with chemical reactions process. The gas absorption is modeled using a vigorous mass transfer theory to represent the realistic behaviors of an absorber. The formed model is a highly nonlinear iterative model which correlates the overall rate of absorption (Rv) as the function of unknown nonideal parameters, including the physical liquid mass transfer coefficient kL and the wetted interfacial area of packings av. The optimization program is developed to minimize the sum of squares of relative errors between the model predictions of Rv and the experimental data. An algorithm for finding the values of optimum parameters in this study follows a modified Levenberg-Marquardt method and an active set strategy to solve the highly nonlinear least squares problem. A system of CO2-NaOH is chosen in this study based on available experimental data. The data were obtained from a pilot plant study of Tontiwachwuthikul. Ceramic Berl Saddle packings (12.7 mm or 1/2") were used in the full length absorber in the pilot plant. Four sets of experimental data are input into an optimization program for estimation of kL and av. The conditions of the operation in the pilot plant are as follows:the concentrations of NaOH are between 1.20 kmol/m to 2.5 kmol/m, liquid flow rates are at 3.75 ร 10 and 2.64 ร 10 m / m sec; gas flow rate is at 1.48 ร 10kmo/l m sec. The physical liquid mass transfer coefficient s is estimated at the range of 6.7 ร 1010to 3.38 X 1010 m sec and the wetted interfacial area of packings av is between 110.-1 to 133.6 m / m It was found that the predictions of the model are in good agreement with the experimental data. The average absolute value of relative error is about 5.4%. Introduction The separation of CO2 from gas mixtures is an essential step in natural gas processing, petroleum refining and petrochemical manufacture. For the production of hydrogen ammonia and synthesis gas which are basic building blocks for the petrochemical industry, the cost of CO2 separation from their gaseous streams can be as high as 30% of the total cost depending on the feed stock. Many technologies such as cryogenic separation, membrane, adsorption and absorption have been developed to separate CO2 on an industrial scale. However chemical absorption of CO2 in liquid solutions followed by stripping of the purified gas is still the most common technology used by industries. The great majority of absorbers used for gas purification are packed, plate, or spray towers. Especially, packed tower are gaining an increasing share of the market due to the development of modern high-capacity, high-efficiency packings.. Although absorption with chemical reaction has been studied for over sixty years the design of a gas absorber using reactive solvents is still largely based on experiments or " rules of thumb" . This is due to the scarcity of fundamental design data such as mass transfer coefficients, interfacial area or other physical-chemical properties.
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Downstream (1.00)
- Management > Professionalism, Training, and Education > Communities of practice (0.42)
- Data Science & Engineering Analytics > Information Management and Systems > Knowledge management (0.42)
- Facilities Design, Construction and Operation > Processing Systems and Design > Separation and treating (0.42)
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Downhole and wellsite flow metering (0.35)
Abstract A knowledge-based or expert system has been developed for intelligent monitoring and control of industrial pipeline network operations. The expert system would perform the supervisory and decision-support tasks based on the expertise and operating procedures that are documented in the maintainable knowledge base. Since it is the first expert system in the pipeline network applications, the pipeline network of a municipal water supply system was chosen as a testing domain due mainly to the experts' availability and safety reason. The stages of system development are described from the knowledge acquisition to the implementation stage. The paper presents an engineering concept of energy management that was applied to build part of the knowledge base in the system. The potential advantages of the expert systems are also listed at the end of this paper. Introduction The pipeline network is one of the most essential components in oil and gas transportation industries. For municipal utilities, the pipeline network system is also an important component that distributes water and natural gas to industrial and business consumers as well as residents throughout the city. The pipeline network system normally consists of a series of pipes (with components such as elbows, tees, valves, etc.), pressurized equipment (pumps or compressors), storage rooms (reservoirs or tanks), and field instruments (flow meters, pressure gauges, level sensors, etc.). Generally, all components are installed in the network at various locations so that a remote monitoring and control system is absolutely necessary. The status of equipment and measurement signals from field instruments are typically sent through modems and telephone lines to a main control station. Operators at the station will monitor the incoming data: when emergencies or changing of process variables occur, the operators will take a series of actions to control the process equipment in order to ensure smooth operations. For the large-scale pipeline network, there would be hundreds of data signals reported to the main control station at every time interval. Ideally, operators should observe all input data reported on the screen(s), analyze them correctly and use them to make proper decision within a few seconds. Of course, the operators who are experts would know how to select the necessary data for solving each type of problems Although expertise is transferable to new operators, some is tacit and difficult to understand within the training courses. The trainees also need a certain period of time to absorb the material and to get used to the system. An expert system for monitoring and control of the pipeline network operations is useful when the most experienced operators will soon resign or retire, and their knowledge and expertise has not been documented and transferred to aspiring operators. This scenario provides the motivation for capturing expertise and heuristic reasoning of the experts on pipeline network operations using artificial intelligent technologies. The paper describes our efforts at this task. Sample domain of pipeline networks The domain addressed is a pipeline network of a municipal water supply system of typical moderate-sized prairie cities in North America.
- Production and Well Operations > Well & Reservoir Surveillance and Monitoring > Downhole monitoring and control (1.00)
- Facilities Design, Construction and Operation > Pipelines, Flowlines and Risers (1.00)
- Data Science & Engineering Analytics > Information Management and Systems > Artificial intelligence (1.00)
Abstract Pipeline networks constitute the major bulk carriers for crude oil, natural gas, water and petroleum products in Western Canada. Each day millions of barrels ofcrude oil billions of cubic feet of natural gas as well as millions of gallons of water are transferred through pipeline networks from the sources to the users. Optimum operations scheduling of these pipeline. Systems can he used to increase the performance and reduce the energy consumption of pumping stations. A key factor in making such systems truly successful is the accuracy a/their demand prediction which is normally implemented with time series analysis. However, for many areas especially in Saskatchewan, weather, special events and other related parameters have major effects on the demand patterns which cannot simply be modeled with the time series techniques. This paper demonstrates how artificial neural networks (ANN) improve the demand prediction of pipeline networks. The water demand patterns were modeled by historical data and related variables using a single continuous perceptron (SCP) and multilayer perceptron (MLP). The implementation was based on real-world data from the City of Regina's pipeline networks. Weights of the SCP model were interpreted to determine relationships between demand patterns and related variables. Finally, comparisons between the models and performance improving techniques were discussed. Introduction The accuracy of demand prediction is an important parameter for the operation planning of pipeline network systems for crude oil natural gas and water transportation. For example the prediction of demand patterns can be used to find optimum pumping schedules. In the implementation of the intelligent system on monitoring and control of the water distribution system at the City of Regina, the knowledge of future demand has great effects on the operation performance of pipeline networks. Most of the existing demand predictors are based on time series analysis, especially the ARIMA (Autoregressive Integrated Moving Average) model. For example. Quevedo et al used Box-Jenkins methodology to predict the water demand of Barcelona city's distribution network. However, it was found that the Box-]enkins's model is very sensitive to noise and is not appropriate for small data set. In addition there arc a few other approaches such as were examined (The intervention components Ie were omitted in Equations 5.2 and 5.3 for clarity): Equation (5.1) (Available in full paper) Equation (5.2) (Available in full paper) Equation (5.3 (Available in full paper) Equation (5.4) (Available in full paper) Equation (5.5) (Available in full paper) Then all the components were combined to predict the water demand as shown below: Equation (5.6) (Available in full paper) For Equation 5.6, the prediction error was reduced to 3.5% of the mean demand value. However, the errors from the other pipeline networks were lying between 4.16% to 6.74%. A Disadvantage of this method is that model testing and modification is time consuming. By contrast, the ANN approach docs not involve human intervention transfer function analysis and model selection because it can automatically capture the model from appropriate training data: autonomous updating is also possible.
- North America > Canada > Saskatchewan (0.27)
- North America > Trinidad and Tobago > Trinidad > Arima > Arima (0.25)