State equipment network

The model is formulated based on the state equipment network (SEN) representation (Yeomans and Grossmann, 1999). The general characterization of this representation includes three elements state, task and equipment. A state includes all streams in a process and is characterized by either quantitative or qualitative attributes or both. The quantitative characteristics include flow rate, temperature and pressure, whereas the qualitative characteristics include other attributes such as the phase(s) of the streams. A task, on the other hand, represents the physical and chemical transformations that occur between consecutive states. Equipment provides the physical devices that execute a given task (e.g., reactor, absorber, heat exchanger). 

This example illustrates the performance of the proposed approach on a single site total refinery planning problem. The refinery scale, capacity and configuration mimic an existing refinery in the Middle East. Figure 7.1 is a state equipment network (SEN)... 

Another important aspect of batch plants relates to the representation of the recipe which is invariably the underlying feature of the resultant mathematical formulation. The most common representation of recipe in the published literature is the state task network (STN) that was proposed by Kondili et al. (1993), which comprises of 2 types of nodes, viz. state nodes and task nodes. The state nodes represent all the materials that are processed within the plant. These are broadly categorized into feeds, intermediates and final products. On the other hand, task nodes represent unit operations or tasks that are conducted in various equipment units within the process. 

In the RTN approach, the process and the plant are represented by networks of different types of nodes which are connected by arcs. The nodes represent states (materials, products), tasks (production steps, operations), or resources (machines, pieces of equipment). 

Chapter 17 - Vapor-liquid equilibrium (VLE) data are important for designing and modeling of process equipments. Since it is not always possible to carry out experiments at all possible temperatures and pressures, generally thermodynamic models based on equations on state are used for estimation of VLE. In this paper, an alternate tool, i.e. the artificial neural network technique has been applied for estimation of VLE for the binary systems viz. tert-butanol+2-ethyl-l-hexanol and n-butanol+2-ethyl-l-hexanol. The temperature range in which these models are valid is 353.2-458.2K at atmospheric pressure. The average absolute deviation for the temperature output was in range 2-3.3% and for the activity coefficient was less than 0.009%. The results were then compared with experimental data. 

Process-scale models represent the behavior of reaction, separation and mass, heat, and momentum transfer at the process flowsheet level, or for a network of process flowsheets. Whether based on first-principles or empirical relations, the model equations for these systems typically consist of conservation laws (based on mass, heat, and momentum), physical and chemical equilibrium among species and phases, and additional constitutive equations that describe the rates of chemical transformation or transport of mass and energy. These process models are often represented by a collection of individual unit models (the so-called unit operations) that usually correspond to major pieces of process equipment, which, in turn, are captured by device-level models. These unit models are assembled within a process flowsheet that describes the interaction of equipment either for steady state or dynamic behavior. As a result, models can be described by algebraic or differential equations. As illustrated in Figure 3 for a PEFC-base power plant, steady-state process flowsheets are usually described by lumped parameter models described by algebraic equations. Similarly, dynamic process flowsheets are described by lumped parameter models comprising differential-algebraic equations. Models that deal with spatially distributed models are frequently considered at the device... 

The state-of-the-art laboratories are equipped with the latest models of analytical instruments and computer systems, while others may have older, less sophisticated equipment or a mix of modern and outdated instruments. The goal of production laboratories is to analyze samples in the fastest possible manner. To be competitive, laboratories must have fully automated analytical systems allowing unattended sequential analysis of samples and computerized output of analytical results. Data acquisition computers, programmed with specialty software, control analytical instruments, collect the raw data, and convert them into analytical results. These computers are typically interfaced with the LIMS, which networks different laboratory sections into a single computer system and transforms analytical results into laboratory reports. 

State lab directors determine whether public health labs in their states should be included in the network. Membership is not automatic. Prospective reference labs must have the equipment, trained personnel, properly designed facilities, and must demonstrate testing accuracy. State lab directors determine the criteria for inviting sentinel labs to join the LRN. 

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