Process Design Research Articles

An investigative study on the effective utilization of process hazard analysis (PHA) and safety instrumentation system (SIS) in chemical process industries

AbstractThis research paper focuses on effectiveness of process hazard analysis (PHA) and safety instrumented systems (SIS) interventions during process design and implementation which prevents accident and loss in chemical process industries. An initial preview was conducted to analyze the accidents and incidents in case of industrial and chemical process industries which enabled to define the research question in a scientific way. The research was conducted using a survey questionnaire developed with the help of focus group and was based on theoretical and conceptual framework developed for the study. The survey received responses from 36 participants who met the inclusion and exclusion criteria. The validity and reliability were established using confirmatory factor analysis, convergent validity, and Cronbach’s Alpha. The data analysis methods were used to answer the research questions to test the hypotheses. From the results it is observed that PHA and SIS were in an agreeable range. However, the performance factors grouped based on barrier controls and organizational behavior were have moderate positive relationship. This showed there exists a gap in the PHA and SIS contributing factors which may directly or indirectly cause industrial accidents in the chemical process industries.

Navigating the Landscape of Organizational Process Mining Setups

AbstractProcess mining (PM) technology evolves around the analysis, design, implementation, and ongoing improvement of business processes. While it has experienced a lot of attention and significant technological advancements, contributions to the field have mostly revolved around technical matters, neglecting managerial and organizational aspects. Thus, researchers have called for a more holistic view of the application and adoption of PM in enterprises. To address this gap, this paper presents a taxonomy for organizational PM setups. Its applicability and usefulness are shown in three exemplary cases. This study extends the descriptive knowledge at the intersection of PM and business process management governance, highlighting the unique governance requirements associated with PM that cannot be effectively addressed through traditional governance approaches. The taxonomy provides practitioners with orientation when developing an effective PM setup and helps to characterize existing setups.

Process Synthesis, Design and Techno-Economic Assessment of Malonic Acid Production

This work focuses on the design and techno-economic evaluation of an industrial facility for the production of malonic acid. The raw material utilized is commercial glucose syrup with a concentration of 95%. Based on a patent of Lygos, Inc., an innovative biotechnology research company, this study presents a comprehensive synthesis, design, and simulation framework for the production of malonic acid through oligosaccharide fermentation. An integrated process flowsheet is proposed and simulated using SuperPro Designer™. The analysis indicates that for an installation capacity of about 8000 MT/yr of the final product with a purity of 99.5%, the production cost is estimated at USD 7.92/kg. A comprehensive study of the capacity’s impact on economics reveals that this cost could decrease to as low as USD 6.05/kg. A parametric analysis and optimization conducted at the flowsheet level identifies opportunities for further reducing production costs, laying the groundwork for a potential decrease in the product’s selling price.

Influence of Temperature and Pressure on the Wetting Progress in 21700 Lithium‐Ion Battery Cells: Experiment, Model, and Lattice Boltzmann Simulation

AbstractThe electrolyte filling and subsequent wetting of the active material is a time‐critical process in the manufacturing of lithium‐ion batteries. Due to the metallic cell housing, the process phenomena are insufficiently accessible, preventing the replication of the wetting processes by mathematical or simulative methods and hindering efforts to accelerate the wetting process. Therefore, this publication employs a glass cell housing for electrolyte filling of a 21700 cylindrical cell to investigate the wetting at different temperatures and process pressures. In parallel, a mathematical replication of the wetting, as well as a lattice Boltzmann pore‐scale simulation, is used to evaluate the influence of these varying process boundary conditions. The results show a strong temperature dependence on electrolyte wetting and the positive effect of pressure changes in the wetting process. These findings are particularly relevant to the process design of large‐scale cylindrical cell manufacturing.

Variable resolution machine learning optimization of antennas using global sensitivity analysis.

The significance of rigorous optimization techniques in antenna engineering has grown significantly in recent years. For many design tasks, parameter tuning must be conducted globally, presenting a challenge due to associated computational costs. The popular bio-inspired routines often necessitate thousands of merit function calls to converge, generating prohibitive expenses whenever the design process relies on electromagnetic (EM) simulation models. Surrogate-assisted methods offer acceleration, yet constructing reliable metamodels is hindered in higher-dimensional spaces and systems with highly nonlinear characteristics. This work suggests an innovative technique for global antenna optimization embedded within a machine-learning framework. It involves iteratively refined kriging surrogates and particle swarm optimization for generating infill points. The search process operates within a reduced-dimensionality region established through fast global sensitivity analysis. Domain confinement enables the creation of accurate behavioral models using limited training data, resulting in low CPU costs for optimization. Additional savings are realized by employing variable-resolution EM simulations, where low-fidelity models are utilized during the global search stage (including sensitivity analysis), and high-fidelity ones are reserved for final (gradient-based) tuning of antenna parameters. Comprehensive verification demonstrates the consistent performance of the proposed procedure, its superiority over benchmark techniques, and the relevance of the mechanisms embedded into the algorithm for enhancing search process reliability, design quality, and computational efficiency.

Retrofit Measures for Aircraft Noise Reduction: Simulation Benchmark and Impact Assessment

DLR, German Aerospace Center, has developed a low-noise retrofit to reduce noise generation of a conventional midrange transportation aircraft, i.e., measures to the airframe and engine exhaust nozzle. Selected measures have been installed onboard of DLR’s flying testbed Advanced Technology Research Aircraft and been subject to a flyover noise campaign. The aircraft in its original configuration and the modified aircraft have been tested and measured in flyover campaigns in 2016 and 2019, respectively. Initial simulation models have been derived from the results and previous component simulations and wind tunnel tests to account for the new reduction measures within DLR’s system noise prediction tool Parametric Aircraft Noise Analysis Module (PANAM). This paper presents the developed measures as selected for the flight test campaigns. An overview of the conducted flight tests is provided. The overall noise simulation process, including aircraft and engine design, flight simulation, and noise prediction, is presented. Simulation results obtained from this process are then compared to the measured levels from the flight campaigns. Estimated simulation uncertainties from PANAM can now be directly compared to differences between measured and simulated levels. Finally, a parameter study is conducted in order to assess the reduction potential of the novel retrofit measures along different simulated flight procedures. Spatial noise distribution in sound exposure level contours is assessed.

Multi-Stage Stochastic Programming Under Endogenous Uncertainty of Integrated Sustainable Chemical Process Design and Expansion Planning

Multi-Stage Stochastic Programming Under Endogenous Uncertainty of Integrated Sustainable Chemical Process Design and Expansion Planning

Multi-Time Scale Energy Storage Optimization of DC Microgrid Source-Load Storage Based on Virtual Bus Voltage Control

The energy storage adjustment strategy of source and load storage in a DC microgrid is very important to the economic benefits of a power grid. Therefore, a multi-timescale energy storage optimization method for direct current (DC) microgrid source-load storage based on a virtual bus voltage control is studied. It uses a virtual damping compensation strategy to control the stability of virtual bus voltage and establishes a virtual energy storage model by combining different types of distributed capability units. The design of an optimization process for upper-level daily energy storage has the objective function of maximizing the economic benefits of microgrids to cope with unplanned fluctuations in power. A real-time energy-adjustment scheme for the lower level is introduced, and a real-time energy-adjustment scheme based on virtual energy storage for the short-term partition of the source-load storage is designed to improve the reliability of microgrid operations. The experiment shows that, in response to the constant amplitude oscillation of the power grid after a sudden increase in power, this method introduces a virtual damping compensation strategy at 20 s, which can stabilize the virtual bus voltage. From 00:00 to 09:00, the battery power remains at around 4 MW, and from 12:00 to 21:00, the battery exits the discharge state. The economic benefits from applying this method are significantly higher than before. This method can effectively adjust the source-load energy storage in real time. During peak electricity price periods, the SOC value of supercapacitors is below 0.4, and during normal electricity price periods, the SOC value of supercapacitors can reach up to 1.0, which can make the state of the charge value of supercapacitors meet economic requirements.

Kinetic study of mineral oil removal from wastewater by the sono-electrochemical process

ABSTRACT Chemical kinetics can be a useful tool for determining the optimal operating time of electrochemical processes. The main objective of the study was to determine the mineral oil removal rate by sono-electrochemical treatment. In this study, zero-, first-, and second-order kinetic models were used to determine the reaction rate of mineral oil removal with the sono-electrochemical process. The reaction rate experiments were conducted under the following optimal conditions: 8 min of treatment time, a current density of 53.1 A/m2, and a flow rate of 0.23 L/s. It was found that the changes in mineral oil concentrations follow second-order kinetics with a coefficient of determination of 0.9732. The mineral oil removal efficiency was 94.4%. This study concludes that sono-electrochemical process could be a promising technology for the removal of mineral oil from wastewater, and that the mineral oil removal rate can be determined by chemical kinetics. The results obtained may be useful for the optimization of the sono-EC process and reactor design.

CRYSTALLIZATION TEMPERATURE AND SOLVENT COMPOSITION EFFECT FOR ENHANCEMENT OF UNSATURATED FATTY ACIDS FROM PALM OIL USING UREA COMPLEXATION

Mono and Poly Unsaturated Fatty Acids (MUFAs/PUFAs) are vital nutritional sources for infants, adults, and the elderly. The content of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) can be enhanced in vegetable oils or animal fats using a range of different techniques. The process of urea complexation is highly effective and efficient for concentrating monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs) from vegetable oils. This study employed refined palm oil as the raw material that went through saponification and purification processes in order to generate fatty acids mixture. Subsequently, urea was introduced to create a complex chemical in the form of urea complexed fraction (UCF) and leaving the non-urea complexed fraction (NUCF). Gas chromatography was employed to analyze the fatty acid composition obtained from urea complexation. This study investigated the impact of different crystallization temperatures (-4°C, 8°C, 12°C, 22°C, and 30°C) and ethanol concentrations (ranging from 0% to 100%) on enhancing the unsaturated content and yield of fatty acids derived from palm oil. It also conducted predictive equations to determine the yield and fatty acids composition in the non-urea complexed fraction (NUCF) which are important for separation process design in chemical industries. The study revealed that increasing the crystallization temperature resulted in a larger yield in the liquid fraction (NUCF), but a lower concentration of monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs).

Real-Time Modeling for Design and Control of Material Additive Manufacturing Processes

The use of digital twin and shadow concepts for industrial material processes has introduced new approaches to bridge the gap between physical and cyber manufacturing processes. Consequently, many multidisciplinary areas, such as advanced sensor technologies, material science, data analytics, and machine learning algorithms, are employed to create these hybrid systems. Meanwhile, new additive manufacturing (AM) processes for metals and polymers, based on emerging technologies, have shown promise for the manufacturing of sophisticated parts with complex geometries. These processes are undergoing a major transformation with the advent of digital technology, hybrid physical-data-driven modeling, and fast-reduced models. This study presents a fresh perspective on hybrid physical-data-driven and reduced order modeling (ROM) techniques for the digitalization of AM processes within a digital twin concept. The main contribution of this study is to demonstrate the benefits of ROM and machine learning (ML) technologies for process data handling, optimization/control, and their integration into the real-time assessment of AM processes. Therefore, a novel combination of efficient data-solver technology and an architecturally designed neural network (NN) module is developed for transient manufacturing processes with high heating/cooling rates. Furthermore, a real-world case study is presented, showcasing the use of hybrid modeling with ROM and ML schemes for an industrial wire arc AM (WAAM) process.

A Security Situation Prediction Model for Industrial Control Network Based on Explainable Belief Rule Base

Industrial Control Systems (ICSs) are vital components of industrial production, and their security posture significantly impacts operational safety. Given that ICSs frequently interact with external networks, cyberattacks can disrupt system symmetry, thereby affecting industrial processes. This paper aims to predict the network security posture of ICSs to ensure system symmetry. A prediction model for the network security posture of ICSs was established utilizing Evidence Reasoning (ER) and Explainable Belief Rule Base (BRB-e) technologies. Initially, an evaluation framework for the ICS architecture was constructed, integrating data from various layers using ER. The development of the BRB prediction model requires input from domain experts to set initial parameters; however, the subjective nature of these settings may reduce prediction accuracy. To address this issue, an ICS network security posture prediction model based on the Explainable Belief Rule Base (BRB-e) was proposed. The modeling criteria for explainability were defined based on the characteristics of the ICS network, followed by the design of the inference process for the BRB-e prediction model to enhance accuracy and precision. Additionally, a parameter optimization method for the explainable BRB-e prediction model is presented using a constrained Projection Equilibrium Optimization (P-EO) algorithm. Experiments utilizing industrial datasets were conducted to validate the reliability and effectiveness of the prediction model. Comparative analyses indicated that the BRB-e model demonstrates distinct advantages in both prediction accuracy and explainability when compared to other algorithms.

Veliparib: Analytical Tools and Process Design Strategies for the Control of Mutagenic Impurities and Other Drug Substance Critical Quality Attributes

Veliparib: Analytical Tools and Process Design Strategies for the Control of Mutagenic Impurities and Other Drug Substance Critical Quality Attributes

Production Technological Design for 500,000 Tonnes Per Year of Ethylene Glycol

Effective and reasonable chemical design of ethtlene glycol production process is of vital significance for the further improvement of our ethylene glycol production capacity. This paper carried out reasonable design of ethylene glycol water lawful production process, using reaction rectifying tower device, and optimizing calculation for each link of production, so as to obtain satisfactory products. In the comprehensive consideration of production efficiency and cost saving and other aspects of the relevant equipment is reasonable selection, and draw the PID process flow chart. It is expected to produce 500,000 tons of pharmaceutical grade glycol with mass fraction of 99.89%.

Mapping design stages and methodologies for developing STEM concept inventories: a scoping review

BackgroundConcept inventories (CIs) have become widely used tools for assessing students’ learning and assisting with educational decisions. Over the past three decades, CI developers have utilized various design approaches and methodologies. As a result, it can be challenging for those developing new CIs to identify the most effective and appropriate methods and approaches. This scoping review aimed to identify and map key design stages, summarize methodologies, identify design gaps and provide guidance for future efforts in the development and validation of CI tools.MethodsA preliminary literature review combined theoretical thematic analysis (deductive, researcher-driven) focusing on specific data aspects, and inductive thematic analysis (data-driven), using emerging themes independent of specific research questions or theoretical interests. Expert discussions complemented the analysis process.ResultsThe scoping review analyzed 106 CI articles and identified five key development stages: define the construct, determine and validate content domain; identify misconceptions; item formation and response processes design; test item selection and validation; and test application and refinement. A descriptive design model was developed using a mixed-method approach, incorporating expert input, literature review, student-oriented analysis, and statistical tests. Various psychometric assessments were employed to validate the test and its items. Substantial gaps were noted in defining and determining the validity and reliability of CI tools, and in the evidence required to establish these attributes.ConclusionThe growing interest in utilizing CIs for educational purposes has highlighted the importance of identifying and refining the most effective design stages and methodologies. CI developers need comprehensive guidance to establish and evaluate the validity and reliability of their instruments. Future research should focus on establishing a unified typology of CI instrument validity and reliability requirements, as well as the types of evidence needed to meet these standards. This effort could optimize the effectiveness of CI tools, foster a cohesive evaluation approach, and bridge existing gaps.

Vapor–liquid phase equilibrium prediction for mixtures of binary systems using graph neural networks

AbstractVapor–liquid phase equilibrium (VLE) plays a crucial role in chemical process design, process equipment control, and experimental process simulation. However, experimental acquisition of VLE data is a challenging and complex task. As an alternative to experimentation, VLE data prediction offers great convenience and utility. In this article, an artificial intelligence network is proposed to predict the temperature and the vapor phase composition of binary mixtures. We constructed a graph neural network (GNN) and designed an uncertainty‐aware learning and inference mechanism (UALF) in the prediction process. The model was tested on both a self‐constructed dataset and a publicly available dataset. The results demonstrate that the proposed method effectively reveals the phase equilibrium properties of the target data. This work presents a novel approach for predicting vapor–liquid phase equilibrium in binary systems and proposes innovative ideas for investigating phase equilibrium mechanisms and principles.

Broadband nonlinear Bragg diffraction from self-assembled structures in potassium tantalate niobate crystals

Nonlinear optical process is a key technology to realize optical communication. Potassium tantalate niobate (KTa1−xNbxO3, KTN) crystals with large nonlinear optical coefficients are potential functional materials for realizing χ2 nonlinear optical processes. However, to accurately modulate the nonlinear optical signals of KTN crystals, the relationship between their second harmonic generation (SHG) properties and ferroelectric domain structures needs to be further investigated. Here, we report the special SHG processes generated by self-assembled structures in KTN crystals. Multimode quasi-phase matching and broadband nonlinear Bragg diffraction are achieved with the changing of polarizations and wavelengths of the fundamental wave. The physical processes behind the multi-polarization and multi-wavelength SHG properties of the KTN crystals are revealed. Our results about the multi-polarization and multi-wavelength SHG properties of KTN crystals will provide guidance for the design and realization of multimode nonlinear optical processes.

Revisiting Aspergillus terreus MTCC6324 recombinant alcohol oxidase (rAOx): enhanced in-silico insight into structure, function, and substrate sequestration mechanism

Alcohol oxidase (AOx) enzymes have gained significant attention for their potential in industrial applications due to their unique ability to catalyze irreversible oxidation of diverse alcohol substrates without external co-factors. This study revisits and enhances in-silico work on Aspergillus terreus MTCC6324 recombinant AOx (rAOx) enzyme, combining artificial intelligence (AlphaFold), molecular docking (AutoDock Vina) techniques and Molecular dynamics (MD) simulations (Desmond). Comprehensive sequence analysis revealed a high degree of conservation among 23 AOx amino acid sequences from various Aspergillus species highlights conserved regions, affirming its GXGXXG Rossmann fold motif. AlphaFold-predicted 3D structure of rAOx demonstrated improved stereo-chemical stability compared to I-TASSER predicted structure with 87.6% amino acid residues in most favourable region of Ramachandran plot compared to 79.5%, respectively. Molecular docking revealed the binding affinities of co-factors FAD and diverse alcohol substrates, with cinnamyl alcohol exhibiting robust interaction with rAOx holoenzyme. MD simulations further elucidate the stability and dynamics of rAOx-FAD-cinnamyl alcohol complex over 100 nanoseconds. The simulations showcase FAD’s stable binding within the protein core and highlights transient substrate interactions, dissociating within the active site after 75 ns suggesting a substrate sequestration mechanism. The study unveils substrate sequestration mechanism wherein cinnamyl alcohol exhibits temporary binding, leading to quick detachment from active site, mimicking reported exponential kinetics. This study not only validates previous findings but also offers a comprehensive understanding of intricate dynamics governing rAOx enzymatic activity. The improved sequence-to-structure prediction and detailed molecular insights into substrate sequestration provide a valuable foundation for future experimental investigations and rational design of bio-catalytic processes.

Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model

Assembly instructions are process documents in detail describing the operation steps, materials, tools, fixtures, and assembly sequences in assembly procedures. Due to assembly instructions including numerous contents, and the content being easy for workers to understand, process designers need to spend lots of time thinking and authoring assembly instructions to ensure that workers can complete the assembly task according to the assembly instructions. Focusing on the difficulties of the variety of assembly instructions and the process factors implicit in the standard languages of assembly instructions, a method of assembly instruction generation for helicopter subassemblies is proposed. First, a data representation model of multi-source heterogeneous knowledge and information based on knowledge graphs is designed and established. Then, a hierarchical pruning VF3 algorithm is presented to reuse assembly instructions according to hybrid similarity. Finally, a process factor revision model based on RoBERTa-BiLSTM-CRF is proposed to generate revised assembly instructions. Helicopter subassemblies, which contain 11,240 assembly procedures, are used to evaluate the performance of the method for generating assembly instructions. The proposed method greatly reduces the time cost of assembly instruction authoring and promotes the intelligent development of assembly process design.

GSOR11 Presentation Time: 12:20 PM: Practice Change from PDR to HDR for Gynecological Brachytherapy Delivery: Process Design and Simulation for Streamlined Transition

GSOR11 Presentation Time: 12:20 PM: Practice Change from PDR to HDR for Gynecological Brachytherapy Delivery: Process Design and Simulation for Streamlined Transition