Industrial process models objectives

The objective function can assume different representation with regards to the system under study. A commonly used objective of an industrial process is to maximize profit or to minimize the overall costs. The former is adopted in this work. In this model, the whole refinery is considered to be one process, where the process uses a given petroleum crude to produce various products in order to achieve specific economic objectives. Thus, the objective of the optimization at hand is to achieve maximum profitability given the type of crude oil and the refinery facilities. No major hardware change in the current facilities is considered in this problem. The... 

Where no complete mathematical description of the process and no dimensionless-numbers equations are available, modeling based on individual ratios can be employed. This is the most characteristic case for a number of industrial processes, especially in the field of organic-chemicals technology. This method is referred to as scale-up modeling (Mukhyonov et al., 1979). In such cases, individual ratios for the model and the object, which should have a constant value, are employed. For instance, there should be a constant ratio between the space velocity of the reacting mixture in the model and the industrial object. Some of the dimensionless numbers mentioned in physical modeling are also employed in this case. 

Simulation results are presented using a model which was fitted to a real industrial process with a 6.3 m reactor. The level set point is at a height of 2.1 m. The objective function is to maximize the component B depletion at the end of the batch and the... 

It is my pleasure and honor to edit this first book on MOO with focus on chemical engineering applications. Although process modeling and optimization has been my research interest since my doctoral studies around 1980, my interest and research in MOO began in 1998 when Prof. S.K. Gupta, Prof. A.K. Ray and I initiated collaborative work on the optimization of a steam reformer. Since then, we have studied optimization of many industrial reactors and processes that need to meet multiple objectives. I am thankful to both Prof. S.K. Gupta and Prof. A.K. Ray for the successful collaboration over the years. 

Also for objects there can be intercorrelations, e.g., related to distance or time, as in environmental sampling and sequential sampling in industrial processes. This property is important for the choice of model, validation, interpretation etc. 

The data obtained from many processes are multivariate in nature, and have an empirical or theoretical model that relates the variables. Such measurements can be denoised by minimizing a selected objective function subject to the process model as the constraint. This approach has been very popular in the chemical and minerals processing industries under the name data rectification, and in electrical, mechnical and aeronautical fields under the names estimation or filtering. In this chapter all the model-based denoising methods are referred to as data rectification. 

The emission of dibenzofurans and dioxins from industrial processes is a major environmental concern. One option to reduce emission levels is to vary the operation conditions in such way that dibenzofurans and dioxins are largely destroyed prior to their release into the environment. Focussing on dibenzofuran, the main objective of this work is to improve our understanding of the general oxidation chemistry and to provide a mechanism suitable for future modelling studies. 

Industrial engineers frequently use simulation experiments to compare the performance of alternative systems and, ideally, to optimize system performance. When a system is modeled as a stochastic process, the objective is often to optimize expected performance, where expected means the mathematical expectation of a random variable. This section describes methods for optimization via simulation, using the problem of selecting the inventory policy that minimizes long-run expected cost per period as an illustration. 

What is OPC This is another tool for system integration. OPC is open connectivity in industrial automation for interoperability supported by the creation and maintenance of open standards and specifications. OPC is a standardized interface for accessing process data. Object linking and embedding (OLE), component object model (COM)/distributed component object model (DCOM), was developed by Microsoft. When this is applied to the process control, OPC (OLE for process control) is developed. OPC is based on the Microsoft COM/DCOM standard and has been expanded according to the manufacturer s requirements. 

The design of PrODHyS follows an industrial software development process based on the use of UML and C++. Currently, the library is successfully used to simulate several kinds of devices. Object philosophy offers a natural approach to process modeling and the ODPTPN formalism eases the management of complex dynamic hybrid simulation. 

In some cases, an actual process variable (such as yield) can be the objective function, and no process model is required. Instead, the process variables are varied systematically to find the best value of the objective function from the specific data set, sometimes involving design of experiments as discussed by Myers and Montgomery (2002). In this way, improvements in the objective function can be obtained gradually. Usually, only a few variables can be optimized in this way, and it is limited to batch operations. Methods used in industrial batch process applications include EVOP (evolutionary operation) and response surface analysis (Edwards and Jutan, 1997 Box and Draper, 1998 Myers and Montgomery, 2002). 

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