Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 1728965 | 11 pages | https://doi.org/10.1155/2019/1728965

Efficacy of Optimal Methods for Nonlinear Equations with Chemical Engineering Applications

Academic Editor: Xianming Zhang
Received14 May 2019
Accepted27 Jun 2019
Published14 Jul 2019

Abstract

In this study, we propose a modified predictor-corrector Newton-Halley (MPCNH) method for solving nonlinear equations. The proposed sixteenth-order MPCNH is free of second derivatives and has a high efficiency index. The convergence analysis of the modified method is discussed. Different problems were tested to demonstrate the applicability of the proposed method. Some are real life problems such as a chemical equilibrium problem (conversion in a chemical reactor), azeotropic point of a binary solution, and volume from van der Waals equation. Several comparisons with other optimal and nonoptimal iterative techniques of equal order are presented to show the efficiency of the modified method and to clarify the question, are the optimal methods always good for solving nonlinear equations?

1. Introduction

Searching out a solution of , when is nonlinear, is highly significant in mathematics; because many equations of that type are common in applied sciences and real life problems. Newton’s iterative technique for solving such equations is defined aswhich has second-order of convergence [1]. Many researchers have improved the method of Newton to attain better results and to increase the convergence order; for instance, see [24] and the references therein. One of the most famous improvements of Newton’s scheme is the technique of order three given in [5]:and another well-known improvement of Newton’s technique is the third-order iterative method proposed by Householder [6]:Recently, many researchers applied the technique of updating the solution to improve the convergence order of the iterative schemes. In this technique, firstly suggested by Traub [1], the composition of two iterative schemes of orders and can yield a new scheme of order . Traub showed that the two-step Newton technique has an order of convergence equal to four. In the same manner and by making a combination of three methods, i.e., Newton, Halley, and Householder, Bahgat and Hafiz [7] presented a three-step iterative method which is of eighteenth-order of convergence; they call their method the predictor-corrector Newton-Halley (PCNH) method. Many examples on iterative methods created using the same technique can be found in [811] and the references therein.

Besides, one of the most common ways to compare the power of iterative methods is the efficiency index which can be determined by , where is the convergence order of the iterative scheme and represents the number of functional evaluations at each iteration. For example, the PCNH mentioned above has an efficiency index equal to . There are some problems that can occur when the technique of updating solution is applied. The main problem is that the number of functional evaluations in each iteration will increase, and consequently the efficiency index will decrease. The second problem which is usually faced is due to the appearance of the second derivative in the numerical scheme. Kung and Traub [8] conjectured that the iterative scheme with the number of functional evaluations equal to is optimal if its order of convergence equals . Many authors have proposed optimal iterative methods of different orders. The default way for constructing optimal method is the composition technique together with the usage of some interpolations and approximations to minimize the number of functional evaluations. Several optimal fourth-order iterative methods were constructed; see, for example, [911]. The optimal eighth-order of convergence was reached by many authors as presented in [1214]. Also, many sixteenth-order iterative methods were proposed; for instance, see [1517].

As we mentioned above, usually, to obtain optimal methods to reduce the functional evaluations at each iteration, researchers use different approximations and interpolations. This process can bring a disadvantage; that is, even if the number of functional evaluations is reduced to the minimum, the number of algebraic operations will be increased. We shall find the answers to the following questions:(i)Are optimal iterative techniques always the best for solving nonlinear equations?(ii)Is it important to minimize the number of function evaluations at each iteration to make the area of convergence of the iterative scheme larger?(iii)Do nonoptimal methods always take longer computational time?(iv)Do optimal methods with equal orders have the same behavior in their dynamics?

We shall in this study answer the above questions by constructing a very simple nonoptimal method of order sixteen using some modifications of PCNH. The work is arranged as follows: The derivation of the modified method is carried out in Section 2. The convergence analysis of the proposed method is discussed in Section 3. Different comparisons with other optimal schemes of equal order are given in Section 4. Comparison using the basins of attraction (dynamics) of the proposed technique and other techniques of equal order is shown in Section 5. Eventually, in Section 6, the conclusion is illustrated.

2. Modified Predictor-Corrector Newton-Halley (MPCNH) Method

Let be an equation such that is a nonlinear function defined on some open interval and sufficiently differentiable. Let be a simple root of , and consider as an initial guess which is sufficiently close to . Using the technique of updating the solution, that is, using Newton’s scheme (1) as a predictor, and both Halley’s scheme (2) and Householder’s scheme (3) as a corrector, Bahgat and Hafiz [7] proposed the following three-step iterative method:Scheme (4) is called the predictor-corrector Newton-Halley (PCNH) method. Bahgat and Hafiz [7] proved that this iterative technique is of eighteenth-order of convergence. Per iteration, PCNH needs the evaluation of three functions, three first derivatives, and two second derivatives. Therefore, the efficiency index of this scheme is equal to . This index is better than for Newton’s method, but worse than that of the classical Halley and Householder methods, which is .

In order to make the efficiency index of PCNH better, we approximate the second derivatives ( and ) using Hermite’s interpolating polynomial of order three. To do that, let , where , and can be found from the following conditions: By solving the system of linear equations resulting from the above conditions, and after substituting the obtained coefficients, one can write asIn the same manner, we can obtain an approximation for :After substituting (6) and (7) into (4), we have the new modified scheme (MPCNH)This method has sixteenth-order of convergence which will be shown in the next section. At each iteration, MPCNH needs the evaluation of three functions and three first derivatives. Therefore, the proposed scheme has efficiency index , which is better than PCNH and both Halley’s and Householder’s methods. Another advantage of this modified method is that it is second derivative free scheme. Note that this modified method is not optimal since it does not satisfy Kung-Traub conjecture.

3. Convergence Analysis

Now, we consider the convergence analysis of MPCNH.

Theorem 1. Let be a simple zero of the function , where is sufficiently differentiable in an open interval . Let be an initial guess close enough to the zero ; then MPCNH technique is at least of sixteenth-order of convergence.

Proof. Let be a zero of , and let be the error at the th iteration. Using the Taylor series about , we getwhere , . From (9) one obtainsFrom (9) and (10) we haveUsing (11) we can write in (8) asExpanding and about and using (12), we getAlso we haveSubstituting (13)–(15) into in (8), we obtainExpanding and about and using (16), we haveAlso we haveSubstituting (16)–(20) in in (8), we getwhich implies thatHence, MPCNH technique is of at least sixteenth-order of convergence.

4. Test Problems and Comparisons

In this section we test the presented technique by applying it on some real life problems resulting from chemical engineering (Examples 14) and other seven arbitrary nonlinear functions (Example 5). Also, we show using different comparisons that optimal methods are not always good for nonlinear equations. For this, we compare the proposed MPCNH method with other five iterative methods of order sixteen; one of them is nonoptimal and the remaining four are optimal. The methods used in the comparison with their abbreviations are the nonoptimal LMMW method of [18], optimal SSSL1 method of [15], optimal GK method of [16], optimal SAK method of [17], and optimal SK method of [19].

We select two convergence conditions for computer programs. The first stopping criterion is , while the second stopping criterion is . All calculations have been performed under the same conditions on Intel Core i3-2330M CPU @2.20 GHz with 4GB RAM, with Microsoft Windows 10, 64 bit, X64-based processor. The software used is Mathematica 9 with 10000 significant digits. Now, consider the following examples.

Example 1 (a chemical equilibrium problem). Consider the equation from [20] describing the fraction of the nitrogen-hydrogen feed that gets converted to ammonia (this fraction is called fractional conversion). Also, consider that we have pressure of atm and temperature of C; the original problem consists of solving the equation which can be reduced in polynomial form asand the four roots of this function are and . By definition, the factional conversion must be between and . Therefore, only the first real root is acceptable and physically meaningful. We started by as an initial guess.

Example 2 (azeotropic point of a binary solution). Consider the problem obtained by [21] to determine the azeotropic point of a binary solution:where and are coefficients in the van Laar equation which describes phase equilibria of liquid solutions. Consider for this problem that and . The root of this equation is . We considered initial approximation .

Example 3 (conversion in a chemical reactor). In this example from [22], the following nonlinear equation is to be solved:where in this equation is the fractional conversion of species, for example, , in a chemical reactor. Therefore, should be bounded between and . The solution of this equation is . As an initial solution, we selected .

Example 4 (volume from van der Waals equation). Van der Waals’ equation is given bywhere are the pressure, volume, temperature in Kelvin, and number of moles of the gas. is the gas constant equal to . Finally, a and b are called van der Waals constants and they depend on the gas type. It is clear that the above equation is nonlinear in . It can be reduced to the following function of For instance, if one has to find the volume of moles of benzene vapor under pressure of atm and temperature of 500°C, given that van der Waals constants for benzene are and , then the problem arising is to find roots of this polynomial.The above equation has three roots which are one real and two complex roots . As is a volume, only the positive real roots are physically meaningful, that is, the first root. We considered the initial approximation for this problem.

Example 5. To study the proposed method on transcendental equations, consider the following seven test examples:

Tables 15 illustrate the comparisons between the iterative methods for Examples 15, respectively, where indicates the number of iterations such that the first stopping criterion is achieved, is the approximate root, is the absolute difference between two successive approximations of the root such that , is the value of the approximate root, the approximated computational order of convergence (ACOC) given in [23], which can be estimated as and, finally, CPU time is the time in seconds required to satisfy the stopping criterion using the built in function “TimeUsed” in “Mathematica 9” software. From Tables 15, we can see clearly that the approximate solutions obtained by MPCNH are more accurate than the estimations obtained by the other five methods. It is also clear that MPCNH needs fewer or equal number of iterations to meet the stopping criterion when compared to the other techniques. In addition, MPCNH needs less CPU time than the other methods to achieve the convergence criterion even if they need the same number of iterations. Also note that MPCNH has ACOC equal to 18 for both and while the other methods have ACOC equal to 16.


Method ACOCCPU Time


LMMWE-200E-3190
SSSL1E-142E-2259
GKE-147E-2340
SAKE-180E-2869
SKE-169E-2690
MPCNHE-211E-3363


Method ACOCCPU Time


LMMWE-197E-3145
SSSL1E-199E-3174
GKE-154E-2462
SAKE-279E-4464
SKE-193E-3078
MPCNHE-199E-3175


MethodACOCCPU Time


LMMWE-335E-5325
SSSL1-diverge
GK-diverge
SAKE-163E-2582
SKE-153E-2416
MPCNHE-207E-3289


Method ACOCCPU Time


LMMWE-438E-7004
SSSL1E-351E-5596
GKE-393E-6280
SAKE-401E-6411
SKE-363E-5794
MPCNHE-487E-8764


MethodACOCCPU Time


LMMWE-70E-1120
SSSL1E-71E-1133
GKE-48E-766
SAKE-91E-1457
SKE-111E-1783
MPCNHE-139E-2230


LMMWE-322E-5153
SSSL1E-191E-3055
GKE-280E-4471
SAKE-319E-4365
SKE-268E-4287
MPCNHE-370E-6657


LMMWE-224E-3585
SSSL1E-69E-1093
GKE-110E-1746
SAKE-134E-2145
SKE-101E-1608
MPCNHE-146E-2618


LMMW