Application of a Monte Carlo Technique in Gas Plant Design

Abstract This paper describes two illustrative examples demonstrating the application of Monte Carlo techniques to gas process design. In addition, a detailed discussion is presented of the characteristics of the Monte Carlo procedure. In the first example Monte Carlo procedure. In the first example Monte Carlo simulation is used to determine the distribution of required MEA circulation rates to sweeten gas in a proposed installation. A simple problem formulation is used; therefore, it is possible to test the sensitivity of the calculated circulation rate distribution to the number of Monte Carlo events and to the selected probability distribution. In the second example, the probability distribution. In the second example, the optimum size of a lean oil absorption plant is determined. Feed rate and composition together with product prices are selected as random variables. Distributions of present-value profit are generated for several plant sizes. profit are generated for several plant sizes. Optimum plant size is selected to maximize the mean present-value profit. Introduction In designing gas processing facilities, plant size or capacity must be established prior plant size or capacity must be established prior to determining process configuration and operating conditions. Establishing the optimum capacity is complicated in that plant inputs and economic constraints, such as feed volume and composition, product price, and equipment operation, may not be known with certainty. This paper demonstrates use of Monte Carlo techniques in gas plant design problems when system inputs are uncertain. To use Monte Carlo techniques in analyzing gas processing projects, plant inputs and economic constraints that characterize the project must first be defined. These project project must first be defined. These project parameters fall into two classes: deterministic parameters fall into two classes: deterministic and stochastic variables. Deterministic variables have a known value, but can vary with time (e.g., the gas price in a long-term gas sales contract). Stochastic variables are not known with certainty and are described by a probability distribution spanning a range of probability distribution spanning a range of values. For most variables a triangular distribution adequately relates the value of a stochastic variable to its minimum, maximum and expected value. Next, appropriate yardsticks for comparing investment decisions must be selected. Present-value profit and investor's interest rate are Present-value profit and investor's interest rate are commonly used economic yardsticks. It may be preferred to use key process variables, plant preferred to use key process variables, plant investment, or product output to characterize performance so that the result may be used in a performance so that the result may be used in a subsequent economic analysis.