A direct search procedure utilizing pseudo random numbers over a region is presented to solve nonlinear programming problems. After each iteration the size of the region is reduced so that the optimum can be found as accurately as desired. The ease of programming, the speed of convergence, and the reliability of results make the procedure very attractive for solving nonlinear programming problems.

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Recently Adelman and Stevens (1972) applied a constrained simplex method to optimize the design of a chemical plant which consisted of a continuous stirred tank reactor, a heat exchanger, a decanter, and a distillation column. The operation of the plant is described by 9 equations in 1 3 variables-FA, FB, FD, FG, FP, FRA, FRB, FRC, FRE, FRP, F R , V , and T . The authors fixed one variable F p at the value 4763 and then considered the optimization of the plant as a nonlinear programming problem. The objective function to be maximized is percent return from the plant.The purpose of this communication is to point out that the mass balance equations are not all independent and that the equations can be rearranged to provide a simple means of solution. Also, we wish to show that the optimal performance index does not change if F P is allowed to change, provided that the other variables are allowed to take on new optimal values. In order that the reader may

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