Mario R. Eden

Reverse problem formulation approach to molecular design using property operators based on signature descriptors

Abstract In this work, an algorithm has been developed for the solution of property based molecular design problems. The recently introduced concept of molecular signature descriptors has been used to design molecules that meet the property targets corresponding to a required process performance. It has been shown that a variety of topological indices (TI) of molecules can be represented in terms of molecular signatures. Signatures can be used to represent different molecular groups if the property targets can be calculated using group contribution models as well. Therefore, the developed algorithm has the ability to combine a variety of property models based on group contribution expressions and topological indices based QSAR/QSPRs to track different property targets in molecular design. This algorithm utilizes molecular property operators formed from signatures for solving the reverse problem of obtaining the molecular structures that satisfy the property targets estimated during the process design step. The principles of graph theory are incorporated to ensure that the design provides feasible molecular structures. Since the molecular operators are formed based on molecular signatures, property models based on different TIs can be represented on the same property platform. Techniques have been developed to describe all TIs with a single signature height. The accuracy of this method depends only on how well the actual property–TI relationships are estimated. Since many TIs can be used to describe each property, this algorithm generally provides reliable results. In addition to physical properties, a wide variety of biological activities can be tracked using the correlations with TIs. This contribution will illustrate the developed methods and highlight their use through a case study.

Targeting optimum resource allocation using reverse problem formulations and property clustering techniques

Abstract Decoupling the constitutive equations from the balance and constraint equations allows for reformulating a conventional forward problem into two reverse problems. The first reverse problem is the reverse of a simulation problem, where the process model is solved in terms of the constitutive (synthesis/design) variables instead of the process variables, thus providing the synthesis/design targets. The second reverse problem (reverse property prediction) solves the constitutive equations to identify unit operations, operating conditions and/or products by matching the synthesis/design targets. Visualization of the problem is achieved by employing recently developed property clustering techniques, which allows a high-dimensional problem to be visualized in two or three dimensions. The clusters by definition satisfy intra-stream and inter-stream conservation through linear “mixing” rules, which allows for the development of consistent additive rules along with their ternary representation.

Simultaneous solution of process and molecular design problems using an algebraic approach

Abstract Traditionally process design and molecular design problems have been treated as two separate problems, with little or no feedback between them. Introduction of the property integration framework has allowed for simultaneous representation of processes and products from a property perspective and hence established a link between molecular and process design. The simultaneous approach involves solving two reverse problems. The first reverse problem identifies the input molecules’ property targets corresponding to the desired process performance. The second reverse problem is the reverse of a property prediction problem, which identifies the molecular structures that match the targets identified in the first problem. Group contribution methods (GCM) are used to form molecular property operators and these help in tracking properties. Earlier contributions in this area have tried to include higher order estimation of GCM for solving the molecular design problem. In this work, the accuracy of property prediction is enhanced by improving the techniques to enumerate higher order groups. Incorporation of these higher order enumeration techniques increases the efficiency of property prediction and thus the range of applicability of group contribution methods to molecular design problems. This method of generation enables the identification of structural isomers to some extent as it puts a check on the possibility of nonexistence of each higher order group in each combination. Property operator based techniques are used to track properties in both process and molecular design problems. The developed algorithm solves the set of inequality expressions of process and molecular design problems simultaneously to identify the molecules that meet the process performance and environmental restrictions defined in terms of properties. Since the algorithm should be able to solve for any number of properties, an algebraic approach is used to generate possible molecules within the required property range. This contribution will use a case study to highlight the principles of the developed methodology.

A novel algorithm for molecular synthesis using enhanced property operators

Abstract Traditionally process design and molecular design have been treated as two separate problems, with little or no feedback between the two approaches. Introduction of the property integration framework has allowed for simultaneous representation of processes and products and established a link between molecular and process design from a properties perspective. Utilizing this methodology enables identification of the desired properties by targeting the optimum process performance without committing to any components. The identified property targets can then be used as inputs for solving a molecular design problem. Earlier works have extended the property integration framework to include group contribution methods (GCMs) for solving the molecular design problem. In this work, second order estimation of GCM has been combined with the first order estimation of GCM using the property clustering methodology in order to increase the accuracy of the property predictions. An algebraic approach has been developed utilizing second order groups built from first order groups subject to the constraints of overlapping. The advantage of using an algebraic approach is that it can handle any number of molecular groups and/or properties and can generate all possible compounds within the required range of properties. The most significant aspect of the aforementioned method is that both the application range and reliability of the molecular property clustering technique are considerably increased by incorporating second order estimation. This contribution will illustrate the developed methods and highlight their use through a case study.

Process synthesis, design and analysis using a process-group contribution method

Abstract This paper describes the development and application of a process-group contribution method to model, simulate and synthesize chemical processes. Process flowsheets are generated in the same way as atoms or groups of atoms are combined to form molecules in computer aided molecular design (CAMD) techniques. The fundamental pillars of this framework are the definition and use of functional process-groups (building blocks) representing a wide range of process operations, flowsheet connectivity rules to join the process-groups to generate all the feasible flowsheet alternatives and flowsheet property models like energy consumption, atom efficiency, environmental impact to evaluate the performance of the generated alternatives. In this way, a list of feasible flowsheets are quickly generated, screened and selected for further analysis. Since the flowsheet is synthesized and the operations in the flowsheet designed through predictive models to match a set of design targets, optimal solution of a given synthesis problem is guaranteed.

Dynamics and control during startup of heat integrated distillation column

Abstract To advocate the usage of process integration in industrial practice, it is important to be able to guarantee not only robust control during near steady state operation, but also to provide procedures for generating fast and reliable startup sequences. This contribution concentrates on describing a systematic procedure for development of plant startup sequences. The basis for the startup procedure development is available qualitative process knowledge. Application of the startup sequence generation procedure is demonstrated upon a heat integrated distillation plant. This plant illustrates some of the inherent effects of process integration upon a startup procedure. In particular, the effect of energy recycle upon the possible startup sequences and the effect of using an actuator for control purposes, i.e. the heat pump, which in itself requires significant startup time. The experimental application of two generated startup sequences illustrates that safe and reliable startups are ensured.

Property Integration—A New Approach for Simultaneous Solution of Process and Molecular Design Problems

Abstract The objective of this paper is to introduce the new concept of property integration. It is based on tracking and integrating properties throughout the process. This is made possible by exploiting the unique features at the interface of process and molecular design. Recently developed clustering concepts are employed to identify optimal properties without commitment to specific species. Subsequently, group contribution methods and molecular design techniques are employed to solve the reverse property prediction problem to design molecules possessing the optimal properties.

Combined property clustering and GC+ techniques for process and product design

Abstract Recent developments in the area of process and product integration have enabled the systematic identification of suitable candidate molecules to meet certain process performance. In this approach, the property targets for the input molecules corresponding to the optimum process performance have been identified in the first step and the molecules that have the target properties have been designed in the next step. The focus of this work is to develop a combined property clustering and GC + algorithm to identify molecules that meet the property targets identified during the process design stage. In our earlier works, a methodology was introduced for identifying molecules with a given set of properties by combining property clustering and group contribution methods. Yet, there are situations when the property contributions of some of the molecular groups of interest are not available in literature. To address this limitation, an algorithm has been developed to include the property contributions predicted by combined group contribution and connectivity indices methods into the cluster space. For the design of simple monofunctional molecules, a modified visual approach has been used, while for the design of more complicated structures an algebraic method has been developed. The applicability of the algebraic method has been increased by including the property contributions from second and third order groups.

A systematic method for integrating product attributes within molecular synthesis

The complete and efficient solution to the enumeration of candidate compounds and mixtures that meet specified consumer attributes is often a difficult mathematical programming problem. Most approaches to this problem involve the solution of a mixed integer non-linear program (MINLP) which may achieve only local optima solutions. In this paper a proof-of-concept study is presented to show that empirical models can be used in a reverse problem formulation to ensure a complete set of candidate compounds and mixtures are found subject to the predictive power of the model. The method utilizes a transformation of consumer attributes to properties described by the group contribution method and solves the reverse problem formulation using the property clustering technique. A case study in refrigerant design is used to highlight the method.

A Systematic Approach to Determine Economic Potential and Environmental Impact of Biorefineries

Abstract The integrated biorefinery has the potential to provide a strong, self-dependent alternative to the use of fossil fuels for the production of chemicals and energy, but difficulties arise in measuring the potential economic and environmental benefit of the biorefinery. A myriad of products and production pathways are possible in this growing field of biorefining, and the production path with maximum value and minimum environmental impact cannot be determined on heuristics alone. A framework is needed to determine the most optimal route based on measures of economic and environmental performance. Gross profit and net present value are used as economic metrics in the short term and long term respectively, and environmental impact is measured using the WAR algorithm developed by Young and Cabezas [1999]. Top candidates in economic and environmental performance are then subject to process integration techniques in order to minimize mass and energy usage, and these integrated biorefineries are once again analyzed for optimal performance.