Data processing: generic control systems or specific application – Specific application – apparatus or process – Article handling
Reexamination Certificate
1999-03-19
2001-01-09
Ellis, Christopher P. (Department: 3651)
Data processing: generic control systems or specific application
Specific application, apparatus or process
Article handling
C137S487500
Reexamination Certificate
active
06173214
ABSTRACT:
FIELD OF THE INVENTION
This invention relates to a batch delivery system, and in particular, to a batch delivery system that maximizes delivery of a material from a material source to a material destination.
PROBLEM
Batch delivery systems are known in which material is delivered from a material source to a material destination by means of automated equipment that controls and measures the amount of material delivered. The problem in a batch delivery process is to maximize the amount of material delivered and to minimize costs of loading and transporting the material. The maximum amount of material delivered is limited by various delivery parameters which include but are not limited to the quantity that the customer has ordered, the physical constraints of the container such as mass or volume, the physical characteristics of the material, and/or any governmental restrictions regulating the transportation of the materials.
It is known that delivery of a material is controlled by monitoring the delivery of material until the material delivered meets or exceeds a certain criterion. A criterion is a constraint in the delivery parameters which must be met in the batch delivery process. The following is an example of a batch delivery system monitoring only one criterion, volume, during the delivery of a material. A cement company wishes to deliver 100 cubic yards of cement to a construction project using the least number of trips with its trucks. Each truck holds a maximum of 5 cubic yards of cement. The obvious solution calls for 20 truckloads of 5 cubic yards each. Unfortunately, the state highway authorities recently fined the cement company for exceeding the 5,000 lb. weight limit per truck. Additionally, the cement company's research shows that the day-to-day variability of the density of the sand and aggregate used in the cement contributed to a day-to-day variability in the density of the cement loaded into the trucks. This variability in density causes some trucks to weigh too much when loaded with the maximum 5 cubic yards of cement. Based on this research, the cement company assumes a worst case scenario for variations in the density of the cement. The cement company decides that a truck would be loaded with no more than 4 cubic yards of cement rather than buying an expensive scale to weigh each individual truck. Thus, it takes 25 truckloads to deliver cement to the construction site to avoid any further fines from the state highway authorities.
It is a problem that these worst case scenarios result in non-optimal transfer of material from a material source to a material destination. In the above problem, a worst case scenario for maximum weight is assumed for a certain volume. Due to normal conditions being better than worst case scenarios, the weight of the cement delivered under normal conditions is less than the weight of the cement delivered in the worst case scenario. The weight of cement delivered under normal conditions is not optimized to the maximum weight allowed. Thus, more cement could be delivered in each cement truck.
Another example of a batch delivery system monitoring only one criterion is a system measuring net solid of a material during the delivery. Net solid is the amount of a certain material (either in suspension or in solution) within a mixture of that solid and at least one other liquid material. In this example, the net solid is the amount of sugar in a mixture of sugar and water. A beverage company orders 25,000 Kg of sugar in a mixture of 40% sugar and 60% water. The container used to deliver the sugar and water mixture to the customer has a maximum volume of 10,000 liters. Analysis showed that the density of the sugar and water mixture would vary from batch to batch. To prevent spillage, the company decides to assume a worst case scenario for density variability of the mixture. After researching the density variability of the mixture, the company decides that the density of the sugar and water mixture in a worst case scenario is 2 Kg/liter. The maximum mass of the mixture to deliver is 20,000 Kg calculated from the maximum volume of the container (10,000 liters) and the density of the sugar and water mixture in the worst case scenario (2 Kg/liter). The net solid amount of sugar to deliver is 8,000 Kg per load calculated from 40% of the 20,000 Kg maximum mass of the sugar and water mixture to deliver. However, the average density of the sugar and water mixture under normal conditions is 2.5 Kg/liter. In an average delivery under normal conditions, the batch delivery system monitors the net solid delivered and delivers 8,000 Kg of the sugar to the container. Thus, in the first delivery, the container only contains 8,000 Kg of the 25,000 Kg of the sugar the customer ordered, and the mixture only occupies 8,000 liters of the container. Assuming normal conditions, the remaining 17,000 Kg will have to be delivered in 3 more deliveries. Once again, the material could not be filled to the 10,000 liter container maximum to account for the worst case scenario of density variability.
It can therefore be seen that it is a problem to monitor only one criterion in the transfer of material from a material source to a material destination which does not result in maximization of the batch delivery.
SOLUTION
The above and other problems are solved in accordance with the batch delivery system of the present invention which maximizes the amount of material to be delivered from a material source to a material destination by monitoring two targets. A primary target is the quantity of the most important constraint in the delivery parameters for the delivery of the material to the destination. Typically, the primary target is the lesser quantity of the amount of material a customer has ordered and a primary constraint of the container or transportation. An example of the primary target is a volume of a container such as 5 cubic yards. The secondary target is the quantity of the second most important constraint in the delivery of the material to the destination. An example of the secondary target is the maximum weight allowed per delivery truck such as 5,000 lbs.
One advantage of the present invention is the amount of material delivered to the destination is maximized while not meeting or exceeding the primary target or the secondary target. In one embodiment of the invention, the primary target and the secondary target are mass, volume, and/or net solid. In one embodiment where the primary target is mass and the secondary target is volume, the invention maximizes the amount of material delivered while accounting for regulatory mass restrictions for transportation, mass limits of the destination, and volume limits of the destination. The invention eliminates any need to assume any worst case scenarios for varying density. The invention accounts for the varying density of the material by not meeting or exceeding the primary target or the secondary target.
In accordance with the present invention, automated control equipment, including a flowmeter and a remotely controlled valve, are placed in series with a path over which the material is delivered to the destination. A batch controller executes instructions for controlling the delivery of the material to the destination in accordance with the present invention. The batch controller receives flow rate information from the flowmeter for the material flowing through the flowmeter. While the flow rate information is being received, the batch controller calculates a primary measurement and a secondary measurement based upon the flow rate information. The primary measurement is the quantity of material delivered through the flowmeter for a specific period of time calculated from the flow rate information in terms of the most important constraint in the delivery parameters. The secondary measurement is the quantity of material delivered through the flowmeter for a specific period of time calculated from the flow rate information in terms of the second most important constraint in the delivery parameters. The batch contr
Campbell Glen
Neelay Dinesh
Duft, Graziano & Forest, P. C.
Ellis Christopher P.
Micro Motion Inc.
Tran Khoi H.
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