Fadwa T. Eljack

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.

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.

Ionic liquid design for enhanced carbon dioxide capture by computer-aided molecular design approach

Carbon capture and storage is an emerging technology to mitigate carbon dioxide (CO2) emissions from industrial sources such as power plants. Post-combustion capture based on aqueous amine scrubbing is one of the most promising technologies for CO2 capture currently. This technology, however, possesses a number of shortcomings, including high regeneration energy requirement, high solvent loss, degradation of solvent, etc. To overcome these limitations, researchers suggested different solvents and alternative technologies to replace the current amine scrubbing technique. Ionic liquids (ILs) are the most potential substitute among all. This is mainly because they have negligible vapour pressure and high thermal stability, which reduce solvent loss. However, there are up to a million possible combinations of cation and anion that may make up the ILs, which makes experimental works very time consuming and costly. In this work, optimal IL solvents specifically for carbon capture purpose are designed using computer-aided molecular design approach. This approach utilises group contribution method to estimate the thermophysical properties of ILs, and UNIFAC model to predict CO2 solubility in the ILs. Structural constraints are included to ensure that the synthesised ILs structure will satisfy the bonding requirement. This work focuses on design of ILs based on a physical absorption mechanism, and hence no chemical reaction is involved. The results show that the designed ILs are capable of capturing CO2 and their predicted properties are in good agreement with properties as determined through experimental works.

Managing Abnormal Operation through Process Integration and Cogeneration Systems

Flaring is a common industrial practice that leads to substantial greenhouse gas emissions, health problems, and economic losses. When the causes, magnitudes, and frequency of flaring are properly understood and incorporated into the design and operation of the industrial plants, significant reduction in flaring can be achieved. In this paper, a process integration approach is presented to retrofit the process design to account for flaring and to consider the use of process cogeneration to mitigate flaring while gaining economic and environmental benefits. It is based on simultaneous design and operational optimization where key flaring sources, causes, and consequences of process upsets are identified then included in the energy profile of the process to design a combined heat and power system with special emphasis on discontinuous sources due to process upset. Environmental and economic benefits are weighed against the cost of process retrofitting. A base case study for an ethylene process is used to illustrate the applicability of the proposed approach and to evaluate the process performance under varying abnormal situation scenarios.

A Property Based Approach for Simultaneous Process and Molecular Design

Abstract In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by only three properties, the process and molecular design problems can be simultaneously solved visually on a ternary diagram, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or “mixing” molecular fragments until the resulting properties match the targets.

Multi-objective optimization methodology to size cogeneration systems for managing flares from uncertain sources during abnormal process operations

Abstract Flaring is common practice in industries to reduce the risk during abnormal situations, to maintain the product quality or to operate safely during process start up and shut down. Due to its large negative impacts on the environment and society, various protocol and steps, i.e., Kyoto protocol, the United Nations Environment Programme, have been created for future mitigation. There is significant amount of heating value lost during flaring events. A cogeneration (COGEN) system can use waste flare streams as fuel to generate heat and power within a process. The objective of this work is to develop an optimization framework for sizing a COGEN unit to manage flares from uncertain sources by minimizing the overall cost and emissions of greenhouse gases. Multi-objective trade-offs between the economic, environmental, and energetic aspects are presented through Pareto fronts for a base case ethylene plant using a stochastic optimization technique based on genetic algorithm.

Property clustering and group contribution for process and molecular design

Abstract In this work, property clustering techniques and group contribution methods are combined to enable simultaneous consideration of process performance requirements and molecular property constraints. Using this methodology, the process design problem is solved to identify the property targets corresponding to the desired process performance. A significant advantage of the developed methodology is that for problems that can be satisfactorily described by three properties, the process and molecular design problems can be simultaneously solved visually, irrespective of how many molecular fragments are included in the search space. On the ternary cluster diagram, the target properties are represented as individual points if given as discrete values or as a region if given as intervals. The structure and identity of candidate components is then identified by combining or “mixing” molecular fragments until the resulting properties match the targets.

A systematic visual methodology to design ionic liquids and ionic liquid mixtures: Green solvent alternative for carbon capture

Abstract Ionic liquids (ILs) have gained great interest recently to substitute volatile organic compounds (VOCs), since their properties can be tuned to match certain targets and applications. Further to this, another possibility to optimise ILs for their specific application is through IL mixtures. In this work, an insightful and yet simple systematic approach to design pure ILs and their mixtures is presented. This newly presented approach allows the visualisation of IL mixture design problem, and hence provides insights and allows users to solve the problem visually. The visualisation of problem and solutions is achieved by applying property integration framework in this proposed methodology. In property integration framework, IL products design problem is mapped from property domain into cluster domain through property clustering technique. Therefore, the proposed methodology provides a property based platform to visualise the overall performance of the designed IL products with graphical tools. A feasible IL product is always designed to fit a purpose based on consideration of multiple target properties, but these properties can be contradicting one another. The presented approach allows multiple target properties consideration during the design process, by portraying these properties and target of each clearly on a single graphical tool. To date, the study of properties of pure ILs and IL mixtures is still in the infant phase, and these data are still scarce. Hence, some of the prediction models do not cover all available ILs. To overcome this problem, the proposed approach is developed to adapt property data of pure ILs directly, together with existing property prediction models to predict the properties of the designed IL mixtures. The presented approach is able to generate a list of potential solutions to users, and the final decision can be made by users accordingly, through further screening and experimental validations. An illustrative case study, which focuses on the design of carbon capture solvents, is solved to demonstrate the proposed approach.

Designing ionic liquid solvents for carbon capture using property-based visual approach

Recently, ionic liquids (ILs) have been introduced as potential carbon dioxide (CO2)-capturing solvents, as a substitute to conventional amine-based solvents. Conventional amine-based solvents that are used for CO2 capture show some drawbacks, such as high solvent loss, high regeneration energy requirement, and solvent degradation. These shortcomings can be potentially overcome if IL-based solvents are considered. ILs have negligible vapour pressure, high thermal stability, and wide range of thermophysical properties. Nonetheless, using experimentation to identify suitable ILs as CO2-capturing solvents is a tedious and costly task, as there are more than a million possible combinations of cations and anions that make up the ILs. Computer-aided tools have been previously developed for targeted IL design, which often involve non-linear programming. However, non-linear programming sometimes fails to converge, due to enlarged search space for optimal solution and its complex formulations. In this paper, the authors present a simple yet systematic visual approach to design IL solvents for carbon capture. Property integration framework is employed in this approach to systematically design IL, where the design problem can be mapped from the property domain into a cluster domain through clustering technique. The advantage of the visual approach is the ability to enumerate novel IL candidates. Group contribution (GC) method is included in the framework to estimate the properties of designed ILs. By combining property integration framework and GC method, the proposed approach is able to provide a property-based platform to visualise the performance of designed ILs on a ternary diagram. A case study is presented to illustrate the validity of the proposed approach.

An Integrated Approach to the Simultaneous Design and Operation of Industrial Facilities for Abnormal Situation Management

Abstract Flaring is a common operation in industrial facilities and is usually intended for safety purposes, for disposal of waste gases with low heating values, or for management of abnormal situations. In addition to the economic losses associated with ineffective combustion of off-spec hydrocarbon products, flaring contributes significantly to the emission of greenhouses gases and primary and secondary pollutants. These are critically important economic and environmental issues to the State of Qatar. Traditionally, abnormal situation management has been addressed through responsive operational strategies. In this work, we propose a novel approach to the optimal management of abnormal situation that occur in facilities, based on simultaneous design and operation. First, the process is described as an integrated system with models that relate the various design and operating variables. The result is a mass-integration framework, which quantifies the causes and consequences of plant upsets resulting in flaring as they pertain to the key objectives of the process, such as productivity, environmental sustainability, safety. Based on this holistic understanding of the process, optimal design modifications and operating strategies are derived for plausible scenarios and deviations. As such, flaring resulting from abnormal situations is reconciled with the various process objectives and is proactively contextualized as part of the design and operation of the process. Once base-case scenarios and designs are developed, dynamic simulation models are developed to track process operation and to determine optimal operational strategies. Additionally, energy integration alternatives (e.g., process cogeneration) will be considered for the effective utilization of heat associated with flaring.