The retrofit design of a network of processes over several time periods is addressed in this paper. A strategy is proposed that consists of a high level to analyze the entire network, and a low level to analyze a specific process flowsheet in detail. A methodology is presented for the high level to model process flowsheets and retrofit modifications using a multiperiod generalized disjunctive programming (GDP) model. This problem is reformulated as a mixed-integer linear program (MILP) using the convex hull formulation. Two examples that illustrate the proposed model are presented. The results show that the proposed GDP model provides a significant benefit over the existing network without retrofit, and provides a clear advantage over intuitively choosing modifications based on heuristics. To illustrate the performance benefits of using the convex hull formulation, the problem is also modeled as an MILP with big-M constraints.

. The process industries (chemicals, food, oil, ...) are characterized by - - continuous or batch -- processes of material transformation. The design of such processes, and their mapping to the available equipment (plants composed of production units in which reactions take place), is a complex process that determines the competitiveness of these industries, as well as their environmental impact. In cooperation with researchers and industry from chemical engineering, we have developed the idea to capture and evaluate the experiences gained about process designs in so-called process data warehouses. The data sources for such process data warehouses are highly heterogeneous tools, e.g. for conceptual design (termed flowsheeting in chemical engineering), for mathematical simulations of large non-linear differential equation systems, for measurements gained with experimental usage of equipment at small scale or in original size, or even from molecular modeling. The clients of a...

Design processes in chemical engineering are hard to support. In particular, this applies to conceptual design and basic engineering, in which the fundamental decisions concerning the plant design are performed. The design process is highly creative, many design alternatives are explored, and both unexpected and planned feedback occurs frequently. As a consequence, it is inherently difficult to manage design processes, i.e. to coordinate the effort of experts working on tasks such as creation of flow diagrams, steady-state and dynamic simulations, etc. On the other hand, proper management is crucial because of the large economic impact of the performed design decisions. We present a management system which takes the difficulties mentioned above into account by supporting the coordination of dynamic design processes. The management system equally covers products, activities, and resources, and their mutual relationships. With respect to coverage and integration, and with respect to the dynamics of design processes, the functionality of the management system goes considerably beyond commercial project, document, and workflow management systems.

ion strategy during local components comparison. The local-root of the component x will be denoted: local-root(x). . function defines the role (the task) of a given component in a flowsheet aggregation. The function of the component x will be denoted: function(x). . quantity defines how many identical components perform the function specified in the function slot. . weight is a real value Î <0;1> that indicates the importance of a given components in the whole assembly. The value of a component x weight will be denoted: weight(x). Additionally each subclass of the component class has domain-specific slots and methods for computing local similarity between two instance components. Thanks to polymorphism the proper domain specific formula is used. Pump Reactor Pump-1 - class - instance - is-a relationship - a-part-of relationship Pump-2 Hydrog. C3 Flowsheet-1 Flowsheet-2 Reactor-1 Reactor-2 Component Hydrog. C6-C8 Process Pipe Pipe-1 Pipe-2 Fig.1. The object-oriented flowsheet repre...

Three applications of evolutionary algorithms, namely the optimization of the nuclear core reload design, the synthesis of multi-layer optical coatings, and the synthesis of chemical engineering plants, are presented in this paper. The examples demonstrate the applicability of the evolutionary approach to solve complex realworld problems. These problems are often mixed-integer, variable-dimensional and multi-criteria optimization problems. Additionally, instead of an objective function, given in a closed form, complex simulation models are used as an objective function, thus prohibiting success by means of classical analysis. Although standard evolutionary algorithms are not able to solve the three complex tasks presented here, enhanced evolutionary algorithms (EAs) clearly demonstrate their potential to do so. Through the three examples, we show the necessary ingredients for tackling real-world optimization problems. In addition to adequate representations and appropriate evolutionar...

In this paper we address the synthesis and design of chemical processes using Chemical Modular Process Simulators – that include state of the art models- including discontinuous cost and sizing equations. Equations are divided into ‘implicit ’ ones which include all the equations in the process simulators with an input-output black box structure, and other third party equations (i.e. sizing and costing correlations for any database) and ‘explicit ’ constraints in form of equalities or inequalities like in any regular equation based optimization environment. Using this modular framework the problem is formulated as a Generalized Disjunctive Programming problem and reformulated and solved as an Mixed-Integer Nonlinear Programming Problem. Different algorithms (Branch and Bound, Outer Approximation and LP/NLP based Branch and Bound) have been adapted to deal with implicit equations and their capabilities have been studied. Several examples are presented in order to illustrate the performance of the algorithms.

This paper describes the adaptation of Evolutionary Algorithms (EAs) to the structural optimization of chemical engineering plants, using rigorous process simulation combined with realistic costing procedures as target function. The main issue for this adaptation relies on the design of a suitable representation of chemical processes as special kinds of parameterized graphs together with corresponding genetic operators. A general catalogue of design guidelines for EA is presented. These guidelines are taken under consideration, when designing the problem specific graph representation and genetic operators. Hereby, the formulation of distance measures on discrete search spaces turns out to be important. Finally, some test runs demonstrate the applicability of the EA, thereby underpinning the usefulness of the design principles.

Process design is usually approached by considering the steady-state performance of the process based on an economic objective. Only after the process design is determined are the operability aspects of the process considered. This sequential treatment of the process design problem neglects the fact that the dynamic controllability of the process is an inherent property of its design. This work considers a systematic approach where the interaction between the steady-state design and the dynamic controllability is analyzed by simultaneously considering both economic and controllability criteria. This method follows a process synthesis approach where a process superstructure is used to represent the set of structural alternatives. This superstructure is modeled mathematically by a set of differential and algebraic equations which contains both continuous and integer variables. Two objectives representing the steady-state design and dynamic controllability of the process are considered. T...

Structuring a four-year, all-inclusive chemical engineering program is elusive. Effective integration of fundamentals prepares students for any chemical engineering topic. Capstone Design and Safety are effective integration courses. Knowledge integration in Design comes through synthesis and evaluation and engineering practice skills development. Problem solving methodology follows a six-step procedure. Synthesis and evaluation are based on a four-tier design evolution. The evolutionary development framework had six structures. Integration is re-enforced in Safety. The topics within the envelope of inherent safety are accident evaluation, hazard identification, probability and consequence estimation, and risk reduction. High-reliability and normal system accident theories are effective tools to allow students to identify sequences of events coupled with chemical engineering fundamentals beyond intended performance. Few curricula have the luxury of adding Capstone Safety. This paper presents a unique integration of Design and Safety. The focus is always on the integration of fundamentals and engineering practice skills. While the emphasis is chemical engineering, the intent and methods should be transferable to other fields.

A combination of bagasse and waste oil, which is usually recovered in the industry from the effluent drains or at the machinery sections, is studied to generate a high CV fuel relative to the low CV bagasse fuel alone. The associated technical aspects such as excess steam generation, stack height requirements, economical returns due to the fuel mix are theoretically assessed in the present study.