Using Linear Genetic Programming to Develop a C / C + + Simulation Model of a Waste Incinerator

We explore whether Linear Genetic Programming (LGP) can evolve a C/C++ computer simulation model that accurately models the performance of a waste incinerator. Human expert written simulation models are used worldwide in a variety of industrial and business applications. They are expensive to develop, may or may not be valid for the specific process that is being modeled, and may be erroneous. LGP is a machine learning technique that uses information about a process’s inputs and outputs to simultaneously write the simulation model, calibrate and optimize the model’s constants, and validate the solution. The result is a calibrated, validated, error-free C/C++ computer model specific to the desired process. To evaluate whether this is feasible for complex industrial processes, the method on data obtained from the operation of a hazardous waste incinerator. This process is difficult to model. Previously, in a well-conducted study, the popular machine learning technique, analytic neural networks, was unable to derive useful solutions to this problem. The present study uses various mutation rates (95%, 50%, and 10%), 10 random initial seeds per mutation rate, and a large number of generations (1,280 to 4,461). The LGP system provided accurate solutions to this problem with a validation data measure of fitness, R, equal to 0.961. This work demonstrates the value of LGP for process simulation. The study confirms previously published results and found that the distribution of outputs from multiple genetic programming (GP) runs tends to include an extended “tail” of outstanding solutions. Such a tail was not found in previous studies of neural networks. This result emphasizes the need for employing a strategy of multiple runs using various initial seeds and mutation rates to find good solutions to complex problems using LGP. This result also demonstrates the value of a fast LGP algorithm implemented at the machine code level for both static scientific data mining and real-time process control. The work consumed 600 hours of CPU time; it is estimated that other GP algorithms would have required between 4 and 136 years of CPU time to achieve similar results. INTRODUCTION With the increasing complexity of modern manufacturing [Popovic98] and processes [Popovic90], industries require fast techniques for adaptive real-time control [Francone00a]. Today, many industries allocate in excess of 10% of their plant investment capital outlays for instrumentation and control [Murrill00]. This percentage has doubled over the past 30 years and shows no signs of diminishing. The industrial processes often are non-linear and the mathematical representation of the process is unknown [Sinha00]. Hence, simulation models are not available for many of the processes that exist today. Without simulation models, optimal process control is very difficult to achieve. This results in unnecessary waste of resources. GP is a promising machine learning technology that has been the subject of intensive academic research since 1988. Since 1998, commercial applications have been made available on the market [Francone00b]. GP can be used to automatically develop a simulation model of complex industrial processes. In this work, we specifically examine whether a very fast form of GP, LGP can evolve a C/C++ computer simulation model that simulates the behavior of the concentrations of carbon dioxide in a waste incinerator. Waste incineration was chosen as a test case because it is a very complex process that involves variable input material properties (i.e., solids, liquids, and gaseous), high temperature, large temperature variations, variable-input energy sources, compressible gas flow, density effects etc. The simulated variable, the concentration of carbon dioxide in the secondary combustion chamber, varies widely from 0 to 5,000 parts per million (ppm). This process has been demonstrated resistant to solution by machine learning via the analytical neural network technique (ANN), as evidenced by a well-conducted study [Fausett00]. Despite the complexity of the incineration processes, developing a computer simulation code that maps the process variables to the carbon dioxide emission concentration should at least be feasible using GP. The strength of GP is its ability to abstract an underlying principle from a finite set of fitness cases [Banzhaf98, Koza99]. This principle can be considered the essence of the regularities that determine the appearance of concrete fitness cases. GP evolves both the structure and the constants of the solution simultaneously, the goal being to extract these Published at the Society for Computer Simulation's Advanced Technology Simulation Conference, Seattle, WA April, 2001 regularities and put them under the form of an algorithm or computer program, which then represents a simulation model. THE GENETIC PROGRAMMING ALGORITHM