Plant optimization models

Another important tool for plant optimization is computer modelling of the economic consequences of process configuration and operation alternatives. The systems of analysis which may bear fruit include the following. 

There are a growing number of commercially available software packages for the above, and of course consultants for application and customization. Significant benefits can be achieved but the costs can be high, as can the time required. Many such programs are well-suited for 

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It is the objective of this paper to provide a comprehensive review of the state-of-the art of short-term batch scheduling. Our aim is to provide answers to the questions posed in the above paragraph. The paper is organized as follows. We first present a classification for scheduling problems of batch processes, as well as of the features that characterize the optimization models for scheduling. We then discuss representative MILP optimization approaches for general network and sequential batch plants, focusing on discrete and continuous-time models. Computational... 

Fig. 8.4 Roadmap for optimization models for short-term scheduling of batch plants.

The bottom-up approach, which develops detailed plant simulation and optimization models, optimizes them, and translates the results from the simulations and optimization into practical operating heuristics. This approach often leads to large models with many variables and equations that are difficult to solve quickly using rigorous optimization algorithms. 

To control, optimize, or evaluate the behavior of a chemical plant, it is important to know its current status. This is determined by the values of the process variables contained in the model chosen to represent the operation of the plant. This model is constituted, in general, by the equations of conservation of mass and energy. 

Alameddine, I., El-Fadel, M. Brine discharge from desalination plants a modeling approach to an optimized outfall design. Desalination 214(1—3), 241—260 (2007)... 

Kouvelis et al. (2004) present a relatively simple multi-period MILP plant location model for global production network design with investment decisions only allowed in the first period. The production system consists of component-dedicated manufacturing sites and final assembly sites. It is limited to two production levels and one final product. The objective function maximizes the NPV of the production network. The main purpose of the model is to analyze the effects financing subsidies, tax regimes, tariff structures and local content requirements have on optimal network design. The analysis is based on theoretical considerations and a numerical example. More complex aspects of international trade such as duty drawbacks are not considered. 

Since scope economies are especially hard to quantify, a separate class of optimization models solely dealing with plant loading decisions can be found. For example, Mazzola and Schantz (1997) propose a non-linear mixed integer program that combines a fixed cost charge for each plant-product allocation, a fixed capacity consumption to reflect plant setup and a non-linear capacity-consumption function of the total product portfolio allocated to the plant. To develop the capacity consumption function the authors build product families with similar processing requirements and consider effects from intra- and inter-product family interactions. Based on a linear relaxation the authors explore both tabu-search heuristics and branch-and-bound algorithms to obtain solutions. 

Furthermore, the link of the shared asset capacity to the overall plant capacity has to be determined. If the equipment capacity is to be selected by the optimization model, an additional integer decision variable is required. Alternatively, a fixed capacity or a capacity correlated with overall plant capacity (e.g., 50% of total plant capacity or one unit for every two production lines) can be assumed. Additional restrictions are required if the correlation approach is to be combined with the modular capacity concept. The choice of the most appropriate variant depends on the characteristics of the equipment considered and the effects on overall model complexity. 

Optimization models are capable of evaluating all possible plant-site combinations simultaneously. However, this approach would considerably increase the data preparation efforts required since for each plant-site combination both investment and operating expenditures would have to be estimated. Additionally, calculation times increase significantly with the number of alternative investment opportunities. 

At the process level, efficient flowsheet optimization strategies based on lumped parameter models are now widely used in practice (Biegler et al., 1997). At this scale, the PEFC is embedded within a power plant flowsheet model, as shown in Figure 3. The process comprises... 

S. Heinrich, M. Ihlow, M. Henneberg, et ah, An optimization model of the operating costs of a fluidized bed-steam drying plant, Can. J. Chem. Eng.,... 

As was described in the review of previous work, over the last ten years MINLP optimization models have been reported for the synthesis of process flowsheets, heat-exchanger networks, separation sequences, reactor networks, utility plants, and design of batch processes. Rather than describing in detail each of these works, we will briefly highlight several examples from our research group at Carnegie Mellon to illustrate the capabilities and the current limitations of the MINLP approach. 

Generically the models considered have a clear, quantitative way to compare feasible solutions. That is, they have single objective functions. In many applications single objectives reahstically model the true decision process. Decisions become much more confused when the problem arises in a complex engineering design, where more than one objective may be relevant. For such cases, as referred above, a multi-objective optimization model is required to capture all the possible perspectives. This is the case of the design of batch plants where two objectives are under consideration - one that maximizes the revenues (that is, production) and the other that minimizes the cost. 

Developing such as model is a preliminary and necessary stage in achieving real time plant optimization which involves treating data reconciliation and rigorous simulation simultaneously by means of optimization techniques, whose objective is to maximize process profitability (Perunicic et.al.,2007). 

A refined method integrating these factors requires a numerical simulation that can estimate any configuration and permit industrial-plant optimization. Different models have been proposed in the literature and we built a versatile simulation software allowing the representation of many different systems (28). 

Simulation results of the algorithm for an enantiomer separation with bi-Lang-muir isotherms are shown in Figure 7.42. The discrepancies between the chromatograms that are predicted by the optimization model and that are assumed for the real plant are shown in Figure 7.43. 

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