General purpose experimental/computational analytical system

• US 20100185430A1
• Filed: 03/30/2010
• Published: 07/22/2010
• Est. Priority Date: 11/04/1993
• Status: Abandoned Application

• Current Assignee: H-Nu Ops, Inc.
• Original Assignee: H-Nu Ops, Inc.
• Inventors: Sproch, Norman K.
• Application Number:
  US12/750,491
• Publication Number:
  US 20100185430A1
• Time in Patent Office: Days
• US Class Current: 703/11
• CPC Class Codes: G01N 33/6848 Methods of protein analysis involving mass spectrometry
• CPC Class Codes: H01J 49/0431 for liquid samples interfaces to liquid chromatographs G01N30/7233
• CPC Class Codes: Y10T 436/24 Nuclear magnetic resonance, electron spin resonance or other spin effects or mass spectrometry
• CPC Class Codes: Y10T 436/25 including sample preparation

Abstract

A general purpose analytical system combining experimental data with a computational simulation or virtual computer model allowing for the solution of unknowns. As illustrated in the chemical examples provided herein, this constitutes a general description for a new type of analytical system that is especially useful for determining experimental unknowns by virtue of analysis involving virtual models or simulations of experiments.

Claims

1. A method for predicting an unknown result of a physical experiment from a computer model or simulation of said experiment, comprising the steps of:

(a) from a first physical experiment, obtaining a first output datum;

(b) utilizing said first output data from the first physical experiment to generate a numerical function describing the relationship between a known input to, and said first output datum of, said first physical experiment;

(c) utilizing a programmed computing device and a display, initializing and running a selected computer program that produces on said display a visual model or simulation of the physical experiment incorporating said numerical function of step (b);

(d) from a second physical experiment, obtaining a second output datum; and

(e) comparing said second output datum from the second physical experiment to a computed output predicted by said visual model or simulation of said second experiment based on a known input for said second physical experiment.

2. The method of claim 1, further comprising the step of:

(f) performing a statistical measurement of correlation between said second output datum from the second physical experiment and the computed output predicted by said visual model or simulation of said second experiment.

3. The method of claim 2, wherein said statistical measure of correlation comprises linear regression.

4. The method of claim 1, wherein if the computed output predicted by said visual model or simulation of said second experiment is found to be outside a predetermined parameter, establishing a feedback loop and initiating an iterative subroutine whereby the visual model or simulation adjusts itself by modifying its program to provide an empirically corrected visual model or simulation.

5. The method of claim 4, wherein said feedback loop is a bidirectional feedback loop.

6. The method of claim 1, wherein the numerical function is modified until the comparing performed in step (e) results in a statistical measurement of correlation between said second output datum from the second physical experiment and the computed output predicted by said visual model or simulation of said second experiment.

7. Utilizing a programmed computing device, a method for aiding in the solution of experimental unknowns using experimental/computational feedback modeling, comprising the steps of:

(a) providing data processing means;

(b) providing data storage means;

(c) performing a physical experiment on a predetermined system;

(d) acquiring raw experimental data from said physical experiment and digitizing the raw data;

(e) storing the digitized raw data in said data storage means;

(f) initializing and running a selected computer program on said programmed computing device that models or simulates the physical experiment being performed step (c);

(g) inputting the digitized raw experimental data stored in step (e) to said data processing means;

(h) computing a new result for the model or simulation of the experiment using the digitized raw experimental data, and storing the new result in said data storage means;

(i) comparing the new result from the model or simulation with the digitized raw data from the experiment performed in step (c);

(j) if the comparison of step (i) is found to be within a predetermined parameter, replacing an undefined parameter from the experiment with the new result from the model or simulation; and

(k) if the result of step (i) is found to be outside a predetermined parameter, establishing a feedback loop and initiating an iterative subroutine whereby the model or simulation adjusts itself, in an incremental way, to fit the model or simulation to the experimental data until the result of the comparison of step (i) is within the predetermined parameter.

8. The method of claim 7, wherein said feedback loop is a bidirectional feedback loop.

9. Utilizing a programmed computing device and a display that depicts a visual model or simulation of an experimental system, a method for solving an experimental unknown using experimental/computational feedback modeling, comprising the steps of:

(a) performing a physical experiment on a predetermined system;

(b) acquiring raw experimental data with selected instrumentation and digitizing the data;

(c) storing the digitized data in a computer memory;

(d) initializing and running a selected computer program that visually models or simulates the physical experiment being performed in step (a);

(e) using the digitized experimental data to compute a new result for the visual model or simulation of the experiment, the results of which are also stored in a computer memory;

(f) comparing the new result from the visual model or simulation with the digitized data derived from the physical experiment;

(g) replacing an undefined parameter from the experiment with the new result in the computer simulation, if the new result is found to be within a predetermined parameter, and;

(h) if the new result is found to be outside a predetermined parameter, establishing a feedback loop and initiating an iterative subroutine whereby the visual model or simulation adjusts itself, in an incremental way, to fit the visual model or simulation to the experimental data until the result of the comparison of step (f) is within the predetermined parameter.

10. The method of claim 9, wherein said feedback loop is a bidirectional feedback loop.