Design and support of systems for operation and maintenance of a cooling petrochemical pumping station

Refineries and petrochemical plants are a very important part of our modern world. Their products include petrol (car fuel), diesel (truck fuel), fuel oil for electrical power plants, benzene, kerosene, chemical fertilizers and the raw material for plastic. These products, and many others, are being used extensively in transportation, energy production, agriculture, manufacturing and many industries including the pharmaceutical industry. Refineries and petrochemical plants generate a lot of heat in the process of making their products. This heat is removed by heat exchangers. For these heat exchangers to work, they need large amounts of water. The water for the heat exchangers is provided by a pumping station. Pumping stations, for the petrochemical industry, pump large amounts of water to the consuming plants. These cooling pumping stations are made of a group of pumps connected in parallel. These pumps deliver cooling water to the consuming plants through a network of pipes. An interruption of cooling water would have severe consequences on the petrochemical industry. Consequently, the reliable delivery of the cooling water can not be over emphasized. To achieve this end, a reliability model for the cooling water delivery must be made. In this thesis, a reliability model for cooling water delivery from a coolingpumping station to a group of petrochemical plants was made. The model took into account the amount of flow that a plant needs to remain operational. A feature of this model is that it is affected by the operational conditions of the lines and valves in the system. As a result, instead of having one reliability model for a plant, each plant would have several reliability models depending on the operational conditions of the lines and valves in the system. The model developed, also, gives a way to look at the reliability of a pumping station having several independent consumers. Reliability modeling is important. Practically speaking, however, it is only a first step. To ensure the reliability of the pumping station, pumps should receive timely maintenance. This timely maintenance is obstructed by the consumer demand of cooling water i.e. there is a conflict between the production function and the maintenance needs of pumps. In this thesis, an attempt was made to minimize this conflict. To minimize the conflict between operation and maintenance, scheduling was used. With scheduling, the operation of pumps around the year would be planned. It was noticed that the consumption of cooling water depended on two things: the weather and a plant’s production level or capacity utilization. Regression analysis was used to find the relationship between a plant’s water consumption and both the weather and production level. The elements of the weather that affected the cooling water consumption of a plant were the ambient air temperature, humidity and seawater temperature. Scheduling is an activity done for future planning. In the case of the pumping station it was for the future planning of pump operation around the year. The relationships developed with regression analysis required the previously mentioned weather factors and a plant’s production level or capacity utilization. While the production levels or capacity utilization of a plant around the year can be obtained from the plant’s owners, the weather factors can not be known in advance. There were two ways to forecast the weather factors: either from a weather service or to develop methods for forecasting these factors. This thesis went with the second option and developed relationships for the local forecasting of the weather factors involved. In this thesis, the attempt to minimize the conflict between operation and maintenance was done by scheduling. The scheduling was done first by using regression analysis to find the relationship between water consumption and a plant’s production and the weather. Secondly, solving this scheduling problem required solving a forecasting problem for the weather factors involved. The results obtained were satisfactory. Increasing the reliability of a pumping station through reliability analysis and minimizing the conflict between operation and maintenance through the previously mentioned methods would help a pumping station in lowering its maintenance expenditure and in improving its service to its consumers. The fact remains, nevertheless, that a pumping station must also generate enough revenue for its owners. Revenue in a pumping station is achieved by selling cooling water to the consumers. The amount of cooling water sold is measured by a flow meter. Flow meters, just like all machines, are susceptible to failure. This failure directly affects the amount of water measured and, subsequently, the revenue. A dangerous type of flowmeter failure is the one that incrementally, but systematically and continuously, alters the readings of a flow meter. This failure is known as flow-meter drift. In this thesis two methods for detecting flow-meter drift were developed: One that used statistical process control (SPC) and the other used artificial neural networks. Both approaches were capable of working with the minimal existing data and were financially inexpensive in their development and application. The first approach, flow-meter-drift detection by using statistical process control, had to transform the widely oscillating data of water demand to a linear form. This was done by creating a virtual mean. The linear, transformed, data were then processed by the SPC method. The method was tested and found satisfactory. The second approach, flowmeter- drift detection by using artificial neural networks, used the same virtual mean developed in the first approach. The linear, transformed, data was further normalized to make the findings universal to all volumes of flow. The normalized output data were then processed by a three layer neural network. The input layer was made of seventeen numerical inputs and seven symbolic inputs. The output layer would show if the flow was normal or drifting upwards or drifting downwards.