Modelling chemical production processes

A chemical production process is a combination of physical and chemical transformation processes. The behaviour of such a transformation system can be described in mathematical terms. To describe the behaviour of a chemical reaction system two general questions have to be answered  

Roughly spoken, the first question can be answered by thermodynamical analyses of the intended reaction, whereas the answers to the second question are typically subsumed 

In the next subsection, kinetic definitions and concepts are introduced. Subsequently, relevant properties of chemical operations w.r.t their modelling are outlined. Special 

---

This chapter provides an overview on basic definitions, terms, concepts, and techniques to describe and model chemical production processes. This allows modelling of the core components in chemical production networks. Figure 2.1 shows an exemplary chemical production network where the production plants are highlighted. 

To model chemical production processes, time series methods are used. Theoretical and empirical indications are given that this class of models is able to capture the characteristic short-term time-dependency patterns of physical and chemical transformation processes. Simultaneously, the models complexity is kept at an acceptable level. To model the change of production modes of chemical production plants in the long run, Markov chains are used. The application of these models is illustrated by real-world case studies. 

Finally, an important characteristic identified in chemical production processes are time-consuming and costly setup operations. Thus, the representation of time is a critical issue in the modeling of chemical production processes. Three fundamental deficiencies of the representation result if continuous processes are modeled by standard bucket-oriented lot-sizing models. These are the carry-over of setup states... 

A production location comprises one or multiple production plants where production resources are located. Production resources are single units or groups of production units aggregated to production lines or assets. Having the structure of chemical commodity value chain network as a network of chemical production processes in mind presented in fig. 34 (Al-Sharrah et al. 2001), production locations include respective resources and transportation lanes between production locations to model relations in chemical Verbund structures. 

To model local chemical production processes at production sites, time series models are used which are able to accurately represent the dynamic, time-dependent structure of chemical production processes. 

This thesis is organized as follows Chapter 2 introduces an overview on chemical production processes. Relevant chemical and physical concepts are introduced which are essential to understand the dynamic nature of chemical reactions. It is shown that time series models are adequate methods to describe the behaviour of chemical production plants. 

Prom Figure 2.1, it becomes obvious that modelling the production plants provides the basic data to describe the material flows within the whole network. Before the chemical production processes are described in detail, some chajacteristics and key figures about... 

In the next section chemical production processes are characterized and categorized. The theoretical modelling of the underlying chemical reactions is described subsequently. Chemical production processes realise chemical reactions in industrial scale in chemical plants. Based on models of chemical reactions, methods are provided to describe the behaviour of chemical production processes. 

Chemical production processes can be divided in chemical reactions and basic operations (i.e. physical transformations). In chemical production plants, multiple processes from both classes are combined and take place in sub-plants which are closely interconnected. The planning and configuration of such plants is very complex and expensive. Hence, a detailed modelling of the underlying chemical and physical processes is necessary to avoid misinvestments. The next section outfines an overview on the steps necessary to... 

Based on thermodynamical and kinetic descriptions of the individual process steps, a meta-model can be developed which is able to describe and predict the behaviour of a whole chemical production process. Such a process model can be developed for different purposes and at different levels of detail To design a chemical production process, a detailed model of the potential plant(s) necessarily includes the description of the system s dynamics. In contrast, once the production process is designed, a model is necessary to describe the dependency of the system s output w.r.t. certain control parameters. Figure 2.6 depicts a prototypical procedure in chemical process modelling. 

Since this work deals with the aggregated simulation and planning of chemical production processes, the focus is laid upon methods to determine estimations of the process models. For process control this task is the crucial one as the estimations accuracy determines the accuracy of the whole control process. The task to find an accurate process model is often called process identification. To describe the input-output behaviour of (continuously operated) chemical production plants finite impulse response (FIR) models are widely used. These models can be seen as regression models where the historical records of input/control measures determine the output measure. The term "finite" indicates that a finite number of historical records is used to predict the process outputs. Often, chemical processes show a significant time-dynamic behaviour which is typically reflected in auto-correlated and cross-correlated process measures. However, classic regression models do not incorporate auto-correlation explicitly which in turn leads to a loss in estimation efficiency or, even worse, biased estimates. Therefore, time series methods can be applied to incorporate auto-correlation effects. According to the classification shown in Table 2.1 four basic types of FIR models can be distinguished. 

One source of mis-specification is the type of the error process. Homogeneity of variances is typically assumed by standard time series models. However, in the context of chemical production processes e.g. an adjustment of a plant s production rate may lead to an imbalance of the underlying chemical reaction(s). This instability may materialize in fluctuations of the output flow rate(s) which may also affect flow rates in subsequent... 

Example 2 (Univariate time series analysis for chemical production data). An application of univariate time series analysis is the modelling of univariate input flows of chemical production processes (as observed for SISO or SIMO processes). To apply prewhitening procedures, the auto-correlation structure of the (exogenous) time series of the input flow has to be modelled. In this example, the input flow rate of a Naphtha cracker is analysed. Figure 2.12 shows the average hourly input flow rate for one week (T = 168). 

In the previous chapters isolated planning problems in chemical industry are described, reviewed and modelled. These approaches allow analysts to model typical chemical production processes and logistical planning problems in chemical production networks. Chemical production networks consist of many chemical plants clustered at chemical production sites. Such networks can be seen as an important part of chemical supply chains (SC). In the scientific literature, there is no unique and concise definition what a SC is, but some common features are prevalent in most definitions ... 

V. V. Kafarov, M. B. Glebov Mathematical Modeling of Basic Chemical Productions Processes, Vysshaya shkola, Moscow (1991) (in Russian). 

Figure 6 presents the basic structure of the BBN model updated and applied for the chemical production process. The results of forward Bayesian inference indicate the potential decrease of the quality of the production when any abnormalities are monitoring within the production process. 

The models presented correctly predict blend time and reaction product distribution. The reaction model correctly predicts the effects of scale, impeller speed, and feed location. This shows that such models can provide valuable tools for designing chemical reactors. Process problems may be avoided by using CFM early in the design stage. When designing an industrial chemical reactor it is recommended that the values of the model constants are determined on a laboratory scale. The reaction model constants can then be used to optimize the product conversion on the production scale varying agitator speed and feed position. 

To tackle these problems successfully, new concepts will be required for developing systematic modeling techniques that can describe parts of the chemical supply chain at different levels of abstraction. A specific example is the integration of molecular thermodynamics in process simulation computations. This would fulfill the objective of predicting the properties of new chemical products when designing a new manufacturing plant. However, such computations remain unachievable at the present time and probably will remain so for the next decade. The challenge is how to abstract the details and description of a complex system into a reduced dimensional space. 

---