Digital Chemical Engineering最新文献

In previous studies, the comprehensive scaling-up of nickel electroforming on a lab-scale rotating disk electrode (RDE) suggested that secondary current distribution could adequately simulate such a forming process. In this work, the use of a 3-D, time-dependent, secondary current distribution model, developed in COMSOL Multiphysics®, was examined to validate the nickel electroforming of an industrial mechanical vane, a low-tolerance part with a demanding thickness profile of great interest to the aerospace industry. A set of experiments were carried out in an industrial pilot tank with computations showing that the model can satisfactorily predict the experimental findings. In addition, these experiments revealed that the local applied current density was related to the surface appearance (shiny vs matt) of the electroform.

Simulations of the process at applied current densities $\le 5A/d{m}^2$ satisfactorily predicted the experimentally observed thickness distribution while, simulations of the process at applied current densities $\ge 5A/d{m}^2$ underpredicted the experimentally achieved thicknesses. Nevertheless, it is proposed that the model can be used for either quantitative or qualitative studies, respectively, depending on the required operating current density on a case-by-case basis. Scanning electron microscopy was used to determine the microstructure of the electroforms and determine the purity of nickel (i.e., if nickel oxide is formed), with imaging suggesting that pyramid-shaped nickel particles evolve during deposition. Another interesting observation revealed a periodicity in the deposit's growth mechanism which leads to “necklace”-like deposit layers at the areas where the electroforms presented the highest thickness.

The calculation of chemical equilibria in detailed reactor simulations frequently requires elaborate numerical solution of the governing equations in an iterative way, which is often computationally expensive and can significantly increase the overall computation time. In order to reduce these computational costs, we introduce a ready-to-use tool, ^ANNH₃, for calculation of equilibrium composition for synthesis and cracking of ammonia based on a neural network. This tool provides excellent agreement with the conventional approach in the range of 135–1000 °C and 1–100 bar and is ca. 100 times faster than conventional stoichiometry-based concepts by replacing the iterative solution process with neural network inference. While speed-up is significant even for the relatively simple case of ammonia synthesis and decomposition, we expect an even higher performance gain for the equilibrium calculation in reaction systems where more components and multiple reactions are involved.

The simultaneous flow of gas, oil & water is frequently encountered in pipelines during upstream petroleum operations. The multiphase flow results in different types of flow patterns based on the flow rates of fluids, physical properties and geometry of the flow domain. The flow behavior is characterized based on the governing flow patterns. Hence, the information about the flow patterns, regime maps and resulting pressure loss are important for multiphase flow system design and optimization. The current work is focused on construction of gas, oil and water, three-phase flow regime maps and developing pressure loss prediction correlations for the flow through vertical riser downstream 90° bend. The pipe internal diameter (ID) is 6 inch and the bending radius to pipe diameter ratio is 1. The observed gas-liquid flow patterns are slug, churn, and semi-annular churn flow at the given range of superficial velocities of fluids. The flow pattern data has been used to construct flow regime maps to analyze the variation in flow patterns with flow rates of fluids and compared with the available works in the literature. In addition, the change in pressure loss with respect to flow patterns has been analyzed. Previous models are used for the prediction of pressure loss. However, according to the assessment, the models underpredicted the pressure loss. Based on three-phase pressure loss data, multiple linear regression analysis has been carried out to propose new correlations for pressure loss prediction. Comparison of the calculated and experimental data showed good agreement between the results. The knowledge of flow regime variation and pressure loss correlations can help flow assurance engineers in designing and optimization of multiphase flow systems.

Particle mixing is a crucial operation in various industrial production processes. However, phenomena like segregation or local accumulation can arise, especially when particles differ in properties like radius and density. Numerical simulation of particles using Discrete Element Method (DEM) allows for the manipulation of control variables in batches, generating a large amount of data and facilitating quantitative research. In this study, the mixing behaviors of binary particles in rotating drums are systematically investigated. The DEM model is first validated with experimental data and then rotating drums with varying obstacles, rotation speeds, particle radii, and densities are simulated. Moreover, a Gaussian process-based optimization is conducted by correlating Lacey mixing index (MI) and parameterized shape of obstacle to find the optimized mixing condition. Experimental validations are further performed on the optimized condition to verify the design. It is shown that this integrated approach holds significant potential for enhancing the efficiency, effectiveness of industrial mixing processes and the consideration of energy consumption when balancing the mixing efficiency and optimal rotating speed.

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

Emission and performance parameters of a 4-stroke CI engine operated on a blend of catalytic co-pyrolysis oil with pure diesel, produced through Azadirachta indica seed, waste LDPE (low-density polyethylene), and aluminium oxide (Al₂O₃) as a catalyst, are modelled in the current work using an Artificial Neural Network (ANN). At 500°C temperature, the highest oil output obtained was 93.91 wt%. The produced liquid fuel possessed similar physical features to that of pure diesel, including density (794 kg/m³) and heating value (44.42 MJ/kg), but lower flash and fire points that would assist in a better and complete combustion of the fuel blend resulting in a better performance and combustion characteristics. Using inputs including brake mean effective pressure, load, brake power, and torque, a developed ANN model was applied to forecast the performance (Brake Thermal Efficiency and Brake Specific Fuel Consumption) along with emission characteristics (Smoke and NO_x). The Levenberg-Marquardt back-propagation training technique was applied for emissions and performance characteristics prediction having the best accuracy. Regression coefficients (R²) for predicting BTE, BSFC, NOx, and smoke were all very near to 1: 0.99801, 0.9983, 0.95753, and 0.97467. The study determines that the proposed alternative fuel could be utilized in blend with the pure diesel to in an unmodified diesel engine. It has also been found that artificial neural networks (ANN) could prove to be useful to model and forecast the performance or emissions of renewable fuels in diesel engines, with the potential for these fuels to be employed in transportation.

This study addresses the challenge of efficiently determining the higher heating value (HHV) of biomass, a crucial parameter in large-scale biomass-based energy systems. The conventional method of measuring HHV using an oxygen bomb calorimeter is time-consuming, expensive, and less accessible to researchers, particularly in developing nations. To overcome these limitations, we employed four machine learning (ML) models, namely Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost). These models were developed by using proximate and ultimate analysis parameters as input features. Up to 200 datasets were compiled from literature and used for the ML models. Our results demonstrate the effectiveness of all ML models in accurately predicting the HHV of biomass materials. Notably, the XGBoost model exhibited superior performance with the highest R-squared (R²) values for both training (0.9683) and test datasets (0.7309), along with the lowest root mean squared error (RSME) of 0.3558. Key influential input features identified for HHV prediction include carbon (C), volatile matter (Vm), ash, and hydrogen (H). Consequently, this research provides a reliable alternative for predicting HHV without the need for costly and time-intensive experimental measurements, facilitating broader accessibility in biomass energy research.

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe₃O₄-Ag/EG, MWCNT-alumina/water-EG, Fe₃O₄-Ag/water-EG, and MWCNT-SiO₂/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R²), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R² values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe₃O₄-Ag/water-EG resulted in an R² value as low as 0.72, to determine the nanofluids’ effective viscosity.

The water-gas shift (WGS) reaction is pivotal in industries aiming to convert carbon monoxide, a byproduct of steam reforming of methane and other hydrocarbons, into carbon dioxide and hydrogen. Selecting an effective catalyst for this transformation poses a substantial challenge, as it requires a delicate balance between conversion, stability, and cost. We combine machine learning-driven prediction models with Bayesian optimization to explore and identify novel catalyst compositions. The proposed method efficiently explores the catalysis composition space for a predefined set of active metals, supports, and promoters to identify the most promising catalyst formulations. We assign weights to different performance metrics of catalysts, enabling tailored optimization according to specific industry needs. Our screening system streamlines catalyst discovery and facilitates the screening and selection of catalysts that balance conversion performance, stability, and cost-effectiveness. This approach holds significant promise for advancement in heterogeneous catalysis to meet the growing demands of efficient industrial processes.

This paper investigates the relationship between Responsible Research and Innovation (RRI) and chemistry / chemical engineering education at university level. It does so by describing the genealogy of the RRI concept as well as outlining three different interpretations of what RRI refers to and combining them into the hexagon model of RRI. This model constitutes the theoretical framework for this work. The second part of the paper addresses how the science and engineering education research literature has embraced insights from RRI. The hexagon model of RRI explicitly includes a dimension on (science and engineering) education, and this paper will contribute to this dimension by investigating and discussing how research literature can link RRI and tertiary chemistry and chemical engineering education. The paper shows that very limited work has been done to liaise chemistry higher education and chemical engineering education with the RRI framework. In the concluding section of the paper, we discuss how the reported educational experiences on RRI in STEM can be translated into higher education in chemical engineering and chemistry. Hereby a proposal to fill the identified knowledge gap is made. The core of the paper is conceptual, and its central purpose is to introduce RRI to a chemical engineering and chemistry ethics education audience. As mentioned, the RRI approach has gone largely unnoticed within engineering ethics education, and only received limited attention within ethics of chemistry education. We hope that these research communities will find it inspirational to get involved in the RRI framework and to actively enact RRI insights.