Williams-Otto process

The Williams-Otto process as posed in this problem involves ten variables and seven constraints leaving 3 degrees of freedom. Three starting points are shown in Table P8.38.1. Find the maximum Q and the values of the ten variables from one of the starting points (S.P.). The minimum is very flat. 

This approach was applied to the Williams-Otto process (Balakrishna and Biegler, 1993). In previous studies, this process was optimized with a CSTR reactor followed by waste and product separators and a recycle stream. The application of (P12) to this problem led to a significantly improved process, particularly when the separation costs p ) were low enough to allow coupled reaction and separation. Without separation, the optimal network is a single PFR with twice the return on investment of previous studies. Allowing for separation leads to a tubular reactor with sidestream separators to remove product and waste as they are created. The resulting process objective has a further fivefold improvement. 

Fig. 1.1 Optimal results of Williams-Otto process for minimizing payback period, PBP and maximizing net present worth, NPW (a) PBP versus NPW, (b) reactor temperature, T, versus NPW and (c) reactor volume, V, versus NPW.

Williams-Otto Process Optimization for Multiple Economic Objectives... 

Keywords Economic criteria, Enviromnental criteria, MOO, Williams and Otto Process, Low-density polyethylene plant. Industrial ecosystems. 

This paper investigate the feasibility using of grey-box neural type models (GNM) for design and operation of model based Real Time Optimization (RTO) systems operating in a dynamical fashion. The GNM is based on fundamental conservation laws associated with neural networks (NN) used to model uncertain parameters. The proposed approach is applied to the simulated Williams-Otto reactor, considering three GNM process approximations. Obtained results demonstrate the feasibility of the use of the GNM models in the RTO technology in a dynamic fashion. 

The mathematical descriptions of each plant unit are summarized in Tables P8.38.2 and P8.38.3. The return function for this process as proposed by Williams and Otto (1960) and slightly modified by DiBella and Stevens (1965) to a variable reactor volume problem is... 

For process optimization with respect to several economic criteria such as net present worth, payback period and operating cost, the classical Williams and Otto (WO) process and an industrial low-density polyethylene (LDPE) plant are considered. Results show that either single optimal solution or Pareto-optimal solutions are possible for process design problems depending on the objectives and model equations. Subsequently, industrial ecosystems are studied for optimization with respect to both economic and environmental objectives. Economic objective is important as companies are inherently profit-driven, and there is often a tradeoff between profit and environmental impact. Pareto-optimal fronts were successfully obtained for the 6-plant industrial ecosystem optimized for multiple objectives by NSGA-ll-aJG. The study and results reported in this chapter show the need and potential for optimization of processes for multiple economic and environmental objectives. 

The above background provides the motivation for the study and applications described in this chapter. Here, two types of process optimization problems are described. The first type has only economic objectives the two examples considered for this are the classical Williams and Otto (WO) process used recently by Pintaric and Kravanja (2006), and an industrial low-density polyethylene (LDPE) plant based on our recent studies (Agrawal et al., 2006 and 2007). The economic objectives tried are PBP, NPW, IRR, profit before taxes, and/or operating cost. The second type has both economic and environmental indices for this, the industrial ecosystem with four plants employed by Singh and Lou (2006) is expanded to an ecosystem with six plants and then optimized for multiple objectives. 

Hahn. Otto (1879-1968) German chemist, who studied in London (with William Ramsay) and Canada (with Ernest Rutherford) before returning to Germany in 1907. In 1917, together with Lise Meitner, he discovered protactinium. In the late 1930s he collaborated with Fritz Strassmann (1902- ) and in 1938 bombarded uranium with slow neutrons. Among the products was barium, but it was Meitner (now in Sweden) who the next year interpreted the process as nuclear fission. In 1944 Hahn received the Nobel Prize for chemistry. 

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