Process Systems Engineering Research Articles

A process systems Engineering approach to analysis of fructose consumption in the liver system and consequences for Non-Alcoholic fatty liver disease

Metabolic disturbances to the liver system can induce lipid deposition and subsequently cause non-alcoholic fatty liver disease (NAFLD). Increasing consumption of fructose has been proposed as a crucial risk component in the development of NAFLD.Three potential therapeutic targets in the network were explored using a composite model of liver function. Introducing a fructose enriched diet under insulin resistance conditions was simulated to evaluate the effectiveness of the model in novel therapy design. In vitro experiments were conducted on rat liver samples to assess the robustness of the model predictions.Synergistic application of all three interventional points in silico has been predicted as the most effective treatment to reduce lipid production under both moderate and severe insulin resistance conditions.This study demonstrates how we can use system models together with in vivo experiments to explore the behaviour of the liver system in response to fructose variation and use it to help identify possible drug targets.

A semantic systems engineering framework for zero-defect engineering and operations in the continuous process industries

The on-going twin transition demands that the continuous process industry builds and operates their facilities in a more sustainable way. This change affects the entire supply-chain. The market demands new ways of engineering, procuring and constructing plants that assure quality at each step of the process. Petroleum and petrochemical producers must reduce their waste and environmental footprint and find ways of migrating to sustainable production. There is zero tolerance for waste, emissions or process malfunctions. Engineering contractors need to transfer their skills to new processes and produce series, non-custom facilities for new applications like offshore wind energy, modular production and industrial symbiosis. This is leading to a convergence in methods with discrete manufacturing, especially the automotive industries. In this climate, this sector can benefit from applying Zero-defect Manufacturing (ZDM) to both engineering design and operations. This work defines a framework for implementing ZDM in the process industry supply chain. The framework brings together modelling techniques and models from the following disciplines: system engineering, computer-aided process engineering, automation (especially Industry 4.0) and semantic technologies. These contributions are synthesised into an information fabric that allows engineering firms to work in new ways. Operators and contractors can use the fabric to move from document-driven engineering to data-based processes. The fabric captures requirements and intent in design so that facilities can be delivered and started-up and operated with zero defects in the design and construction. The information is also a vital support for safe and efficient operations and maintenance. We call this zero-defect O&M. The framework combines a systems engineering break-down of facilities, based on ISO/IEC81346, with implementation in SysML, with semantic interoperability frameworks from the process industries (ISO15926). We build upon and synthesise the results of recent standardization initiatives from the industry, notably CFIHOS, DEXPI and READI. We draw on results from process systems engineering, the OntoCAPE ontology and the CAPE-OPEN standards. The framework is illustrated by application to a non-proprietary process system, namely the Tennessee-Eastman process. This example is used to show the modelling approach and indicate how the fabric supports zero-defect practices.

Simultaneous and collaborative development of satellites and Information Systems to support their lifecycle processes

Space Systems Engineering is the domain in which satellites are developed. A lot of information gathered by the systems engineer to develop a space product is also necessary and gathered by the software engineer in the Software Engineering domain to develop the Information Systems that will support the lifecycle processes of that product. Motivated by this reality, this paper aims to demonstrate that the work of the software engineer can be integrated with that of the systems engineer during the Systems Engineering process. We assumed the use of Model-Based Systems Engineering (MBSE) and Systems Concurrent Engineering (SCE) as the approaches to engineering a satellite and looked at the activities performed by the systems engineer to identify how the software engineer’s work can be inserted into the Systems Engineering process. We use the AMAZONIA 1 satellite to exemplify and focus on the process of assembly, integration and testing of this satellite. We concluded that simultaneous and collaborative work between the Systems Engineering and Software Engineering domains is feasible and can bring quality and productivity gains for both sides.

Membrane-assisted natural gas liquids recovery: Process systems engineering aspects, challenges, and prospects

Membrane-based natural gas liquid (NGL) recovery processes are still far from their large-scale applications owing to communication gaps among academic researchers and industry practitioners. A comprehensive process systems engineering (PSE) assessment of membrane-based NGL recovery processes is required to determine their commercial suitability. This PSE-based review presents the technical and economic aspects of standalone and integrated membrane processes. Literature review shows that polymeric membranes (e.g., cellulose acetate) are primarily evaluated in NGL recovery processes despite their low separation efficiencies. So far, multiple multistage membrane models with standalone and integrated designs have been suggested by analyzing different configurations to improve separation efficiency. In standalone processes, cellulose acetate membrane modules with high selectivity ratio can improve methane recovery by up to 100%. Absorption or cryogenic integrated processes exhibit high methane recovery (up to 99%) but demonstrate high energy consumption. The integrated absorption–membrane process is more capital cost intensive (i.e., 0.41 m$) than the cryogenic–membrane process (0.39 m$). Furthermore, in this review, the key challenges encountered by membrane processes and related issues are identified to improve their commercial viability by capitalizing on their maximum potential benefits. The major challenges associated with membrane processes constitute the lack of rigorous multistage membrane models and inflexibility in product purity and recovery. The policy implications and future directions suggest that owing to the growing demand for NGLs, membranes that can sustain varying natural gas compositions and conditions may be required. This PSE assessment will help process engineers and policymakers to improve natural gas supply chain economics.

Process Model Inversion in the Data-Driven Engineering Context for Improved Parameter Sensitivities

Industry 4.0 has embraced process models in recent years, and the use of model-based digital twins has become even more critical in process systems engineering, monitoring, and control. However, the reliability of these models depends on the model parameters available. The accuracy of the estimated parameters is, in turn, determined by the amount and quality of the measurement data and the algorithm used for parameter identification. For the definition of the parameter identification problem, the ordinary least squares framework is still state-of-the-art in the literature, and better parameter estimates are only possible with additional data. In this work, we present an alternative strategy to identify model parameters by incorporating differential flatness for model inversion and neural ordinary differential equations for surrogate modeling. The novel concept results in an input-least-squares-based parameter identification problem with significant parameter sensitivity changes. To study these sensitivity effects, we use a classic one-dimensional diffusion-type problem, i.e., an omnipresent equation in process systems engineering and transport phenomena. As shown, the proposed concept ensures higher parameter sensitivities for two relevant scenarios. Based on the results derived, we also discuss general implications for data-driven engineering concepts used to identify process model parameters in the recent literature.

Prospects of Producing Higher Alcohols from Carbon Dioxide: A Process System Engineering Perspective

Prospects of Producing Higher Alcohols from Carbon Dioxide: A Process System Engineering Perspective

On the Analysis and Assessment of First-Order Group Contribution Models for the Calculation of Normal Boiling Point and Critical Properties of Pure Compounds

The thermodynamic properties of pure compounds are relevant data for process systems engineering. Different first-order group contribution models have been reported in the literature to calculate these properties and they are also widely employed in commercial process simulators. However, they may have some limitations and, consequently, a reliable comparison of these models is required to analyze their performance and to determine the best alternative for the calculation of pure compound properties. This paper reports the implementation and evaluation of several first-order group contribution models to calculate the normal boiling point and critical properties (temperature, pressure, and volume) of pure compounds. The performance of these models was characterized and compared for several compound families using a standardized approach to determine their group contributions and parameters. An artificial neural network model was also applied and assessed to improve the estimations obtained with the best group contribution models. Results showed that the calculation of critical temperature was challenging for several compound families where AARD values ranged from 0.05 to 56.28%, while the group contribution models were more accurate to estimate the critical volume with AARD values ranging from 0.48 to 35.99%. This study allows us to identify the limitations and gaps of this type of thermodynamic models with the objective of improving its performance for the calculation of pure compound thermodynamic properties. The findings of this study can help to enhance the capabilities of thermodynamic models for the calculation of the normal boiling point and critical properties of pure compounds, which are relevant for the process systems engineering of new operations and products.

Data-driven scenario generation for two-stage stochastic programming

Optimisation under uncertainty has always been a focal point within the Process Systems Engineering (PSE) research agenda. In particular, the efficient manipulation of large amount of data for the uncertain parameters constitutes a crucial condition for effectively tackling stochastic programming problems. In this context, this work proposes a new data-driven Mixed-Integer Linear Programming (MILP) model for the Distribution & Moment Matching Problem (DMP). For cases with multiple uncertain parameters a copula-based simulation of initial scenarios is employed as preliminary step. Moreover, the integration of clustering methods and DMP in the proposed model is shown to enhance computational performance. Finally, we compare the proposed approach with state-of-the-art scenario generation methodologies. Through a number of case studies we highlight the benefits regarding the quality of the generated scenario trees by evaluating the corresponding obtained stochastic solutions.

Continuous Manufacturing of Solid Drug Products — from Process Systems Engineering Perspectives

Continuous Manufacturing of Solid Drug Products — from Process Systems Engineering Perspectives

Guidance for Systems-Engineering Processes for Subsea Production Systems

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 31827, “Systems Engineering of Subsea Production Systems,” by Amedeo Marcotulli, Saipem, and David Wilkinson, Endeavor Management. The paper has not been peer reviewed. Copyright 2022 Offshore Technology Conference. Reproduced by permission. As the complexity and cost of subsea production systems (SPS) has increased during recent decades, the requirement for a more-rigorous, systematic approach toward the engineering of such systems has increased. A systems-engineering (SE) guidance document, specifically written to be applicable to SPS, is being developed by members of API Sub-Committee 17 at the time of writing. The objective of the complete paper is to make potential users of the guidance document aware of the benefits associated with the use of formal SE processes when designing complex facilities. SE Fundamentals Systems engineers exercise a high-level understanding of how system elements work together, visualizing complex systems as a set of subsystems that interact recursively. Recursive relationships are critical in SE and define many hierarchical taxonomies, such as the following: - System components consist of subsystems - Project activities consist of subprojects - Organizations consist of suborganizations - Contracts consist of subcontracts However, an elegance exists in the recursive nature of SE because its processes are self-similar and independent of scale. Its fractal nature allows application of the same process at any level and for any size of project, as shown in Fig. 1. The recursive and iterative nature of SE results in information flows in four directions: - Downward path: Requirement information flows from top to bottom, until the lowest elements are fully defined. - Upward path: Integration information flows from the lowest-level element. - Forward path: Interface information flows across elements that are at the same level, in each level. - Backward path: Feedback information informs the design and realization processes on every iteration for continuous improvement. SE formalizes the interfaces between disciplines, streamlining communications and avoiding rework. SE takes control of the iterations and recursions, thus minimizing waste. Therefore, SE reduces engineering cost while optimizing engineering design. SE requires a mindset more akin to an architect’s duties than to those of an engineer. It requires synthesis more than analysis, involving a top-down view of the system that goes further than an intimate analysis of its parts.

Advancements in Mission Assurance Standards for Expendable and Reusable Launch Vehicles

The U.S. Space Force’s Space and Missile Systems Center’s Launch Enterprise and The Aerospace Corporation (Launch Enterprise/Aerospace) continue to advance the systems engineering, mission assurance, and acquisition processes for National Security Space Launch (NSSL) services. The Launch Enterprise/Aerospace team has proactively evolved the mission assurance process to embrace new capabilities such as reusable launch vehicles and additive manufacturing. The mission assurance process also evolved to incorporate new technologies, increase flexibility, reduce cost, and incorporate lessons learned for application to expendable launch vehicles and reusable launch systems. This paper provides a top-level summary on 1) the advancements made to the mission assurance process, 2) the development of new technical standards, 3) the tailoring of standards, and 4) the adoption of standards to address lessons learned. Furthermore, the application of these advances to the NSSL acquisition of launch services is also discussed because it is a key U.S. Space Force objective to launch every National Security Space satellite with high reliability and low risk to achieve full operational capability.

Sustainable food cold chain logistics: From microenvironmental monitoring to global impact.

Food cold chain logistics (FCCL) is a systematic engineering process involving the use of a low-temperature environment to maintain the quality and safety of perishable food and reduce food loss and waste (FLW). From a mechanism perspective, FCCL must balance resource costs for a required level of food quality and safety with the costs of greenhouse gas (GHG) emissions. In the context of global warming, the sustainability trade-off between FLW and environmental impact has recently become an important topic in research on efficient, green FCCL. This is mainly reflected in technological innovation, management optimization, and policy responses. With a focus on three levels (micro, meso, macro), this review analyzes current research areas and the gaps and challenges of FCCL in microenvironmental monitoring, life cycle assessment (LCA), and global impact. Future trends pertaining to FCCL in technology, management, and industry and sustainable development are also summarized. Future trends involving sustainable FCCL must be intelligent, systematic, and low carbon. Industry empowerment through next-generation information technologies (e.g., IoT, AI, big data, blockchain) will promote the multidimensional perception, real-time information transmission, and sustainable control of microenvironmental monitoring, as well as support LCA management transformation from fragmentation to system integration. From a macro level, due to the serious global loss of perishable food, the FCCL scale demand is growing greatly, causing a huge environmental burden. Global cooperation, low-carbon consensus, and appropriate policies will become the basis for promoting sustainable FCCL development.

Challenges and opportunities in carbon capture, utilization and storage: A process systems engineering perspective

Carbon capture, utilization, and storage (CCUS) is a promising pathway to decarbonize fossil-based power and industrial sectors and is a bridging technology for a sustainable transition to a net-zero emission energy future. This work provides an overview of process systems engineering (PSE) research challenges, advances, and opportunities for CCUS applications. We review PSE methods, tools, and techniques in process modeling, simulation, optimization, control, material screening, strategic planning, and supply chain network design for CO2 management. We also attempt to give a PSE perspective on emerging CCUS research interests in molecular and materials systems engineering, multiscale modeling and optimization, systems design and integration under uncertainty, and the application of intelligent systems. The purpose is not to cover all aspects of PSE research for CCUS but rather to foster discussion by presenting some plausible future directions and ideas.

A review of group contribution models to calculate thermodynamic properties of ionic liquids for process systems engineering

Ionic liquids are compounds with interesting physical and chemical properties that can be applied to a wide variety of processes. The knowledge of their thermodynamic properties is essential for the design of products and processes. These properties include heat capacity, density, viscosity, thermal conductivity, melting point, surface tension, electrical conductivity, refractive index, thermal decomposition temperature, normal boiling point, critical properties, freezing point, isobaric expansivity, isothermal compressibility and static dielectric constant. However, the experimental database available of such properties for ionic liquids is limited, thus affecting the process design and modeling. Different thermodynamic models have been developed to estimate these properties and, group contribution models offer several advantages for these applications. This review covers different group contribution models reported and applied to estimate the thermodynamic properties of ionic liquids. The application, performance, and accuracy of these models for predicting the ionic liquids properties were analyzed and discussed. Some approaches combining group contribution models with artificial neural networks to estimate the thermodynamic properties of ionic liquids have been also described and exemplified. This review offers a survey of a variety of group contribution approaches that can be used to predict several thermodynamic properties of ionic liquids for process systems engineering.

Faulty scenarios in sour water treatment units: Simulation and AI-based diagnosis

Fault Detection and Diagnosis (FDD) is a Process System Engineering (PSE) area of great importance, especially with increased process automation. It is one of the chemical engineering fields considered promising to Artificial Intelligence (AI) application. FDD systems can be useful to supervise Sour Water Treatment Units (SWTU) behavior, as they are chemical processes that present operational difficulties when disturbances occur. SWTU remove contaminants from sour water (SW) streams generated through petroleum processing, consisting mainly of small amounts of H2S and NH3. They are considered one of the primary aqueous wastes of refineries and cannot be disposed of due to environmental regulations. However, no previous studies focused on the development of FDD systems for SWTU exist and works on its dynamics are scarce. Hence, the present work proposes to study the dynamic simulated behavior of an SWTU and develop an FDD system applying AI techniques with hyperparameters optimization. The simulation was performed in Aspen Plus Dynamics® and ran to create normal operation and six relevant faults, including occurrences in the process (e.g., inundation and fouling) and sensors. FDD was performed through data classification, and results were evaluated mainly by accuracy and confusion matrices. Even after variable reduction, FDD was satisfactory with over 87.50% accuracy in all AI techniques. RF and SVM with linear and Gaussian kernels presented the best results, with over 93% of accuracy in training and testing, and had the shortest computing times. The second column’s sump level proved to be the most relevant variable for fault identification.

Addressing the contribution of agricultural systems to the phosphorus pollution challenge: a multi-dimensional perspective

The intensification of agricultural systems has increased the food production efficiency, increasing the productivity while the production costs are reduced. Although these factors are key to global food security in a context of continued human population growth, the use of intensive agricultural techniques results in different environmental issues. Mitigating these negative impacts is a requirement for adopting sustainable food production systems. Notably, nutrient pollution is one of the main environmental issues associated with both livestock and crop production. These activities result in different point and non-point source releases of phosphorus, which eventually reach surface and ground waterbodies. This might result in the accumulation of phosphorus over time, contributing to the eutrophication of water ecosystems, and the development of harmful algal bloom (HABs) episodes. The releases of nutrients from agricultural activities can be abated through different management strategies, including the implementation of nutrient recovery techniques at livestock facilities, embracing precision fertilization methods, and developing integrated crop-livestock systems for achieving circular food production systems. In this work, we describe opportunities for Process System Engineering (PSE) to address the development of phosphorus management techniques for mitigating phosphorus pollution from agricultural systems balancing trade-offs between recovery cost and environmental impact mitigation. These techniques integrate the spatial analysis of nutrient pollution from agriculture using geographical information systems (GIS) with the assessment and the selection of phosphorus management techniques combining techno-economic analysis (TEA) and environmental metrics through multi-criteria decision analysis (MCDA) frameworks, and use mathematical programming for the conceptual design of integrated crop-livestock systems.

Transient Analysis of a Supercritical Carbon Dioxide Air Cooler Using IDAES

Abstract Supercritical CO2 offers a promising solution for meeting modern energy demands. Used as a working fluid in Brayton cycles, it yields greater net efficiencies than traditional atmospheric air or flue gas. To achieve this, however, compressor inlet conditions must be kept close to the critical point (7.32 MPa and 31 °C), posing a design and operations challenge. To cool the working fluid, most designs rely on ambient air which fluctuates in temperature. In this paper, we present a model of one such air cooler that can be used as a digital twin. Our model captures two-dimensional, cross flow, and counter-current geometry while also accounting for heat holdup in the tube and fin material. This was achieved using entirely free, open-source software, namely the Institute for the Design of Advanced Energy Systems (IDAES) process systems engineering (PSE) framework. We demonstrate our model’s capability with transient simulations of air temperature changes, predicting CO2 properties at the compressor inlet. Results show an asymmetric, non-linear response in CO2 temperature and density, and highlighting the difficulty in working near the fluid’s critical point.

Chemical engineering at crossroads

AbstractThrough periodic introspection and assessment, the chemical engineering field has developed a mature undergraduate curriculum built on a strong science background in mathematics, physics, and chemistry. This brings a unique set of skills in transport, reaction engineering, and thermodynamics, coupled with suitable process systems engineering and process design courses, to supply well‐trained engineers to a vast array of process manufacturing facilities. These facilities produce basic chemicals, pharmaceuticals, oil and gas, petrochemicals, food and agricultural products, minerals, and materials. While this maturity has served existing industries well, we argue that the chemical engineering field is at crossroads between managing the curriculum of undergraduate and graduate education to supply the needs of established industries while creating innovators for emerging industries. While this is a great opportunity for yet another introspection, we caution that the inadvertent cannibalization of the field must be avoided. We do argue in favour of adding a biology sequence and a computational science sequence to the core at the undergraduate level in a related perspective article.

Remembering Dr. Hans Fauske

Process Safety ProgressVolume 41, Issue 3 p. 602-602 LETTER TO THE EDITOR Remembering Dr. Hans Fauske Dilip K. Das, Corresponding Author Dilip K. Das dilipkdas@gmail.com Emergency Relief System Design and Process Engineering, Design and Safety, LLC, Kansas City, Missouri, USA Correspondence Dilip K. Das, Emergency Relief System Design and Process Engineering, Design and Safety, LLC, 4701 NE Shady Lane Drive, Kansas City, MO 64119, USA. Email: dilipkdas@gmail.comSearch for more papers by this author Dilip K. Das, Corresponding Author Dilip K. Das dilipkdas@gmail.com Emergency Relief System Design and Process Engineering, Design and Safety, LLC, Kansas City, Missouri, USA Correspondence Dilip K. Das, Emergency Relief System Design and Process Engineering, Design and Safety, LLC, 4701 NE Shady Lane Drive, Kansas City, MO 64119, USA. Email: dilipkdas@gmail.comSearch for more papers by this author First published: 06 July 2022 https://doi.org/10.1002/prs.12400Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume41, Issue3September 2022Pages 602-602 RelatedInformation

Oversimplification of Systems Engineering Goals, Processes, and Criteria in NASA Space Life Support

AbstractThis paper investigates the oversimplification of the inherently complex systems engineering process in space life support. The standard systems engineering process steps are described. The International Space Station (ISS) life support system is explained with its goals and performance criteria. Although it is not usually emphasized, the essential function of developing a hierarchy of systems and subsystems is to simplify the design process. The System Complexity Metric (SCM) shows how this divide‐and‐conquer approach also reduces the system complexity. The complete systems engineering process has many detailed steps. It is often simplified because of the effort required and the human limitations on working memory and decision span. Systems analysis demands slow, logical, and focused thinking but is often bypassed in favor of quick, intuitive, subconscious “gut feel.” A study of 100 system designs found examples of 12 specific mental mistakes, such as ignoring stakeholder needs, and these mistakes are essentially oversimplifications of the systems engineering process. An analysis of space life support goals, options, criteria, and processes found 11 examples of oversimplifications in systems engineering, such as neglecting safety and cost. All these 11 oversimplifications could be traced to one or more of the 12 previously identified mental mistakes or other well‐known ones, such as ignoring sunk costs. Oversimplification of the systems engineering process is rarely noticed but is a common and harmful problem. A study of failures in 50 different space systems found that problems in systems engineering caused failures and often led to errors in design, development, and test that further contributed to failure. It seems that more diligent systems engineering could prevent many project problems and failures, but projects seem to be more guided by “gut feel” based on tradition, authority, and consensus than on the logical, rational systems engineering approach.