Author: claimsbot

1. A computer-readable storage medium storing instructions executable by a computing system to perform a plurality of functions, comprising:
receiving a calculation rule presented in a multi-platform expression language to be applied to a data model selected from different data models having different structures, the calculation rule being received by one or more of:
retrieving the calculation rule from a store of previously-created calculation rules; and
accepting user entry of the calculation rule;
processing the calculation rule for application to the data model, including:
responding to whether the calculation rule is data-driven, including:
when the calculation rule is data-driven, determining a condition specified by the calculation rule and performing an action specified by the calculation rule based on the condition; and
when the calculation rule is not data-driven, performing the action specified by the calculation rule;
identifying a database platform supporting the data model; wherein the database platform is selected from a group of platforms comprising: a structured query language (SQL) platform that accesses a SQL database in which dimensions and data models are represented as a plurality of tables and a multi-dimensional database platform that accesses a multi-dimensional database in which the dimensions and the data models are stored in a multi-dimensional structure;
preprocessing the calculation rule to adjust the calculation rule to operate based on a dimensionality of the data model; wherein preprocessing the calculation rule comprises determining when the calculation rule accesses more information than the data model provides and determining when to eliminate the calculation rule based on this determination;
upon identifying one or more variables included in the calculation rule, resolving the variables in terms of the data model; and
upon identifying one or more parameters included in the calculation rule, soliciting input for the parameters for use in applying the calculation rule to the data model by displaying a prompt to receive the parameters; and
executing the calculation rule.
2. The computer-readable storage medium of claim 1, wherein the identifying of the selected platform comprises receiving a user input identifying the selected platform.
3. The computer-readable storage medium of claim 2, wherein the identifying of the selected platform comprises automatically identifying the selected platform.
4. The computer-readable storage medium of claim 2, further comprising evaluating data maintained in the data model to determine whether the data model maintains data referenced in a function specified in the calculation rule before executing the function.
5. The computer-readable storage medium of claim 2, wherein the calculation rule includes an engine including a plurality of scripted rules configured to perform a transaction-based function.
6. A computer-implemented method, comprising:
receiving a calculation rule presented in an expression language and specifying one or more functions, the expression language allowing for each of a plurality of functions performable on a plurality of different platforms to be expressed using a common syntax; wherein the different platforms comprise: a structured query language (SQL) platform that accesses a SQL database in which dimensions and data models are represented as a plurality of tables and a multi-dimensional database platform that accesses a multi-dimensional database in which the dimensions and the data models are stored in a multi-dimensional structure;
identifying a selected platform on which the calculation rule is to be executed;
resolving the calculation rule to attributes of a data model in the selected platform to apply the calculation rule to the data model; wherein resolving the calculation rule comprises determining when the calculation rule accesses more information than the data model provides and determining when to eliminate the calculation rule based on this determination;
responding to whether the calculation rule is data-driven, including:

when the calculation rule is data-driven, determining a condition specified by the calculation rule and performing an action specified by the calculation rule based on the condition; and when the calculation rule is not data-driven, performing the action specified by the calculation rule;
executing the calculation rule by performing the one or more functions specified in the calculation rule; and
reporting the results from executing the calculation rule.
7. The computer-implemented method of claim 6, wherein the expression language provides for the calculation rule to include one or more variables resolvable when the calculation rule is applied to the data model.
8. The computer-implemented method of claim 6, wherein the expression language provides for the calculation rule to include one or more parameters solicited from a user when the calculation rule is applied to the data model.
9. The computer-implemented method of claim 6 wherein the identifying of the selected platform comprises receiving a user input identifying the selected platform.
10. The computer-implemented method of claim 6, wherein the identifying of the selected platform comprises automatically identifying the selected platform.
11. The computer-implemented method of claim 6, further comprising evaluating data maintained in the data model to determine whether the data model maintains data referenced in the one or more functions before executing the calculation rule.
12. The computer-implemented method of claim 6, further comprising retrieving the calculation rule from a store of previously-created calculation rules.
13. The computer-implemented method of claim 12, wherein the calculation rule includes an engine including a plurality of scripted calculation rules configured to perform a transaction-based function.
14. The computer-implemented method of claim 6, further comprising creating the calculation rule, wherein the calculation rule is derived from one or more rule types that correspond with a form one or more data types used in creating the data model.
15. A system, comprising:
a processor and a computer-readable storage medium;
a rule interface using the processor that is configured to receive a calculation rule presented in a multi-platform expression language to be applied to a data model, the calculation rule being received by one or more of:
retrieving the calculation rule from a store of previously-created calculation rules; and
accepting user entry of the calculation rule; and
a run-time system configured to:
receive the calculation rule from the rule interface;
identify a database platform supporting the data model; wherein the database platform is selected from a group of platforms comprising: a structured query language (SQL) platform that accesses a SQL database in which dimensions and data models are represented as a plurality of tables and a multi-dimensional database platform that accesses a multi-dimensional database in which the dimensions and the data models are stored in a multi-dimensional structure;
identify whether the calculation rule includes one more unresolved terms; upon identifying the calculation rule includes one or more unresolved terms, resolving the unresolved terms;
adjust the calculation rule for execution on the data model; wherein adjusting the calculation rule comprises determining when the calculation rule accesses more information than the data model provides and determining when to eliminate the calculation rule based on this determination; and
identifying whether the calculation rule is data-driven, including: when the calculation rule is data-driven, determining a condition specified by the calculation rule and performing an action specified by the calculation rule based on the condition; and when the calculation rule is not data-driven, performing the action specified by the calculation rule.
16. The system of claim 15, wherein the run-time system further comprises a preprocessor configured to adjust the calculation rule to operate based on a dimensionality of the data model.
17. The system of claim 15, wherein the identifying of the database platform is performed one of:
by receiving a user input identifying the database platform; and
automatically identifying the database platform.
18. The system of claim 15, wherein the one or more unresolved terms include:
a variable resolvable to the data model; and
a parameter to be solicited from a user.
19. The system of claim 15, further comprising a type library, including:
one or more rule types from which the calculation rule is derived; and
one or more data types from which members of the data model are derived, variable resolvable to the data model,

wherein the rule types and the data types are configured to correspond to facilitate application of the calculation rule to the data model.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. A model for simulating the volatile organic content (VOC) of polyolefin product stream discharging from a purge column of a polyolefin polymerization system, the model comprising:
an interface component configured to provide for input to the model and for output from the model; and
an iterative calculation component based on at least mass transfer theory and configured to represent removal of volatile organic components from a polyolefin stream flowing through the purge column, and to provide a calculated value of the VOC in a polyolefin product stream discharging from the purge column.
2. The model as recited in claim 1, wherein the inputs to the model comprise a flow rate of the polyolefin stream through the purge column and a flow rate of a purge gas through the purge column, the purge gas flowing counter current to the polyolefin stream.
3. The model as recited in claim 1, wherein the input to the model comprises a temperature of a flash gas discharging overhead from a flash vessel disposed upstream of the purge column, and wherein the temperature of the flash gas is indicative of a temperature of the polyolefin stream entering the purge column.
4. The model as recited in claim 1, wherein the model estimates a height of a separation stage in the purge column and a number of separation stages in the purge column.
5. The model as recited in claim 1, wherein the input to the iterative component is an initial estimate of a concentration of a hydrocarbon component in the polyolefin product stream discharging from the purge column.
6. The model as recited in claim 1, wherein the input to the iterative component is an initial estimate of a concentration of a hydrocarbon component in a hydrocarbon-rich purge gas exiting from the purge column.
7. The model as recited in claim 1, wherein the polyolefin stream enters an upper portion of the purge column and the polyolefin product stream exits a bottom portion of the purge column, and wherein a purge gas enters a bottom portion of the purge column and exits as a hydrocarbon-rich purge gas from an upper portion of the purge column.
8. The model as recited in claim 1, wherein the interface component comprises a spreadsheet.
9. A method for operating a polyolefin manufacturing process, comprising:
discharging a slurry from a polyolefin reactor, wherein the slurry comprises polyolefin and hydrocarbon;
recovering polyolefin from the slurry by flashing at least a majority of the hydrocarbon from the slurry, the recovered polyolefin having residual hydrocarbon;
purging the recovered polyolefin in a purge column with a purge gas to remove a first portion of the residual hydrocarbon from the recovered polyolefin;
discharging a polyolefin product stream from the purge column, the polyolefin product stream having a second portion of the residual hydrocarbon, wherein the second portion comprises a volatile organic content (VOC) of the polyolefin product stream; and
estimating the VOC of the polyolefin product stream via a mass-transfer model.
10. The method as recited in claim 9, comprising adjusting a residence time of the recovered polyolefin in the purge column in response to the estimated VOC of the polyolefin product stream discharging from the purge column.
11. The method as recited in claim 9, comprising adjusting a flow rate of purge gas to the purge column in response to the estimated VOC of the polyolefin product stream discharging from the purge column.
12. The method as recited in claim 9, comprising adjusting a temperature of the recovered polyolefin in the purge column in response to the estimated VOC of the polyolefin product stream discharging from the purge column.
13. The method as recited in claim 12, wherein adjusting a temperature of the recovered polyolefin in the purge column comprises adjusting an operating temperature of an upstream flash vessel that separates flashed hydrocarbon from the slurry discharging from the polyolefin reactor.
14. The method as recited in claim 12, wherein adjusting a temperature of the recovered polyolefin in the purge column comprises adjusting an operating temperature of an upstream flash line heater that flashes hydrocarbon from the slurry discharging from the polyolefin reactor.
15. The method as recited in claim 9, comprising changing catalyst type employed in a polymerization of olefin monomer in the polyolefin reactor in response to the estimated VOC of the polyolefin product stream discharging from the purge column.
16. A method for operating a polyolefin manufacturing process, comprising:
discharging a product slurry from a polymerization reactor, the product slurry comprising polyolefin particles and hydrocarbon;
receiving the product slurry in a flash vessel and separating flashed hydrocarbon from the product slurry in the flash vessel;
discharging a first stream of the polyolefin particles from the flash vessel, the first stream comprising residual hydrocarbon;
purging the first stream in a purge column with a purge gas to remove the residual hydrocarbon;
discharging a second stream of the polyolefin particles from the purge column; and
estimating the volatile organic content (VOC) of the second stream via a mass-transfer model.
17. The method as recited in claim 16, comprising adjusting an operating condition of the purge column in response to the estimated VOC.
18. A process for manufacturing a product comprising a polyolefin, the process comprising:
manufacturing a product at least a portion of which comprises a polyolefin, wherein the polyolefin is produced by a method comprising:
discharging a slurry from a polyolefin reactor, wherein the slurry comprises polyolefin and hydrocarbon;
recovering polyolefin from the slurry by flashing at least a majority of the hydrocarbon from the slurry, the recovered polyolefin having residual hydrocarbon;
purging the recovered polyolefin in a purge column with a purge gas to remove a first portion of the residual hydrocarbon from the recovered polyolefin;
discharging polyolefin product from the purge column, the polyolefin product having a second portion of the residual hydrocarbon, wherein the second portion comprises a volatile organic content (VOC) of polyolefin product; and
estimating the VOC of the polyolefin product via a mass-transfer model.
19. The process as recited in claim 18, wherein manufacturing comprises processing the polyolefin to form the product or to form a polyolefin component of the product.
20. A polyolefin production system comprising:
a polymerization reactor configured to polymerize olefin monomer in a diluent in the presence of a catalyst to produce polyolefin;
a separator configured to receive a product slurry discharged from the polymerization reactor and to separate at least diluent vapor from the product slurry, the product slurry comprising polyolefin;
a purge column configured to receive polyolefin from the separator and remove at least residual diluent from the polyolefin; and
a model configured to provide a calculated value of a volatile organic content (VOC) of the polyolefin discharged from the purge column.

1. A computer implemented method having instructions executable by a processor for receiving streaming data, comprising:
receiving a source data stream from a source computer;
receiving one or more peer data streams from one or more peer computers, wherein the one or more peer computers are assigned a color by using a colored overlay, wherein a peer computer produces and sends packets of a peer data stream in the color corresponding to the peer computer;
forming a cooperative peer to peer relationship among the peer computers to receive a data stream, wherein the peer computers schedule a departure instance of sending the data stream, wherein the cooperative peer to peer relationship is a peer-paired pyramid streaming architecture comprising the peer computers coupled at a same level as one or more peer pairs;
combining at least a portion of the one or more peer data stream with at least a portion of the source data stream to derive a complete data stream;
detecting a packet loss and recovering a lost packet, wherein a first peer computer affected by the loss requests a duplicate packet of the packet loss from a second peer computer before issuing a retransmission request to the source computer, wherein if the lost packet is not recovered after exchanging a request to the second peer computer, a receiver issues the retransmission request to the source computer;
using a controlled delay between two sessions to the first peer computer and to the second peer computer to handle a burst loss, wherein the two sessions comprise a same content, which are destined for different peer computers, wherein the controlled delay is adjusted based on a current network status; and
receiving different portions of the data stream, wherein the peer computers exchange streaming data with another peer computer to form the complete data stream, provided the receiver received an incomplete data stream;
wherein the different portions of the data stream is not larger than the portion of the source data stream for aggregated bandwidth efficiency.
2. The computer implement method as recited in claim 1, wherein the source computer is a parent computer in a hierarchical computer system, and the peer computer is a sibling computer in the hierarchical computer system.
3. The computer implemented method as recited in claim 1, wherein complete data stream further comprises a bandwidth equal to a source bandwidth provided by the source computer.
4. The computer implemented method as recited in claim 1, wherein:
the source data stream further comprises a first bandwidth;
the peer data stream further comprises a second bandwidth;
the first bandwidth is greater than the second bandwidth.
5. The computer implemented method as recited in claim 1, further comprising providing a client data stream to the peer computer, wherein the client data stream is a subset of the source data stream.
6. The computer implemented method as recited in claim 5, wherein the client data stream does not contain streaming data that is included in the peer data stream.
7. The computer implemented method as recited in claim 5, wherein the peer data stream includes packet data that was originally included in the source data stream but an error prevented the packet data from being received or rendered.
8. The computer implemented method as recited in claim 7, wherein the packet data is received in the peer data stream in response to a request for said packet data.
9. The computer implemented method as recited in claim 1, farther comprising selecting the peer computer from a plurality of system computers by using the colored overlay to determine the most appropriate system computer to utilize as a peer computer.
10. A computer implemented method having instructions executable by a processor for receiving peer to pyramid streaming of data, comprising:
receiving a first streaming data having a first bandwidth from a server;
receiving a second streaming data having a second bandwidth from a peer computer, wherein the peer computer schedules a departure instance of sending the second streaming data, wherein the peer computer is assigned a color by using a colored overlay, wherein the peer computer produces and sends packets of a data stream in the color corresponding to the peer computer;
merging the first and the second streaming data to play the streaming data at a final bandwidth that is greater than the first bandwidth and the second bandwidth;
transmitting at least a portion of the first streaming data to the peer computer;
wherein the first streaming data and the second streaming data do not contain common data and a size of the first streaming data is larger than a size of the second streaming data;
detecting a packet loss and recovering a lost packet, wherein the peer computer requests a duplicate packet of the packet loss from a second peer computer before issuing a retransmission request to the source computer, wherein if the lost packet is not recovered after exchanging a request to the second peer computer, a receiver issues the retransmission request to the source computer; and
increasing a streaming speed by each node receiving data from one or more upstream nodes and by receiving mutually parity packets from each sender;
wherein the different portions of the data stream is not larger than the portion of the source data stream for aggregated bandwidth efficiency.
11. The computer implemented method as recited in claim 10, wherein the first bandwidth is greater than the second bandwidth.
12. The computer implemented method as recited in claim 10, wherein the second streaming data farther comprises one or more data packets that are duplicates of lost data packets provided in the first streaming data but rendered unplayable by a streaming error.
13. The computer implemented method as recited in claim 10, farther comprising requesting a retransmission of the lost data packets from the server in the event that the lost data packets cannot be successfully recovered from the peer computer.
14. The computer implemented method as recited in claim 10, further comprising:
identifying a request from the peer computer to transmit a certain portion of the first streaming data to the peer computer; and
transmitting the requested portion of the first streaming data to the peer computer.

The claims below are in addition to those above.
All refrences to claim(s) which appear below refer to the numbering after this setence.

1. An equalization filter structure for filtering an audio signal within an audio system, the equalization filter comprising:
a first and a second shelving filter having a fixed first and a fixed second phase response, each of which is determined by a respective cut-off frequency and Q factor which represent the transfer characteristic of the corresponding shelving filter;
wherein the first and the second shelving filters are coupled in series and each shelving filter comprises (i) at least one fourth order low-pass filter having a cut-off frequency, the Q factor and a first broadband gain and (ii) at least one fourth order high-pass filter having the same cut- off frequency and the same Q factor as the low-pass and further having a second broadband gain;
wherein the fourth order low-pass filter and the fourth order high-pass filter are connected in parallel, such that both filters receive the same input signal and the corresponding filtered signals are summed to form a respective shelving filter output signal; and
wherein each fourth order low-pass and high-pass filter is composed of a cascade of two second order low-pass or high-pass filters, respectively, each second order filter having the same cut-off frequency and the Q factor as the corresponding shelving filter.
2. The equalization filter structure of claim 1, wherein the cut-off frequencies and the Q factors of the filters comprising one shelving filter are, separately for each shelving filter, pre-defined and fixed and the broadband gains are variable to adjust the magnitude response for equalizing the audio signal in a desired manner.
3. The equalization filter structure of claim 1 wherein the first and the second shelving filter are a bass and a treble shelving filter, respectively.
4. The equalization filter structure of claim 3 wherein the cut-off frequencies and the Q factors are fixed and predefined for the shelving filters and the broadband gains are individually adjustable for each of the shelving filters.
5. The equalization filter structure of claim 1, wherein each shelving filter comprises:
a second fourth order low-pass coupled in series to the first fourth order low-pass; and
a second fourth order high-pass coupled in series to the first fourth order high-pass.
6. The equalization filter structure of claim 5, wherein the fourth order low-pass filter of the first shelving filter has a broadband gain G, the fourth order high-pass filter of the first shelving filter has a broadband gain G\u22121, the fourth order low-pass filter of the second shelving filter has a broadband gain G\u22121, and the fourth order high-pass filter of the second shelving filter has a broadband gain G, thus implementing a peaking filter with a constant phase characteristic, the magnitude response of the peaking filter being adjustable by adjusting the broadband gain G.
7. A method for equalizing an input audio signal associated with one respective audio channel of an audio system, the method comprising:
filtering the input audio signal using a first and a second shelving filter having a fixed first and a fixed second phase response, each of which is determined by a respective cut-off frequency and Q factor which represent the transfer characteristic of the corresponding shelving filter;
wherein the first and the second shelving filters are coupled in series and each shelving filter comprises (i) at least one fourth order low-pass filter having a cut-off frequency, the Q factor and a first broadband gain and (ii) at least one fourth order high-pass filter having the same cut-off frequency and the same Q factor as the low-pass and further having a second broadband gain;
wherein the fourth order low-pass filter and the fourth order high-pass filter are connected in parallel, such that both filters receive the same input signal and the corresponding filtered signals are summed to form a respective shelving filter output signal; and
wherein each fourth order low-pass and high-pass filter is composed of a cascade of two second order low-pass or high-pass filters, respectively, each second order filter having the same cut-off frequency and the Q factor as the corresponding shelving filter.
8. The method of claim 7, wherein the cut-off frequencies and the Q factors of the filters comprising one shelving filter are, separately for each shelving filter, pre-defined and fixed, and the method further comprises:
adjusting the broadband gains to adjust the overall magnitude response of the shelving filters for equalizing the audio signal in a desired manner.