Chemical Engineering Problems Research Articles

Exploring tubular steady‐state laminar flow reactors with orthogonal collocation

AbstractThe laminar flow reactor (LFR) is one of the most comprehensive problems in chemical reaction engineering, as its modeling involves mass, heat, and momentum conservation equations coupled with chemical reaction rate equations. It is relatively easy to grasp its basic operation, but the solution of the problem is far from trivial. Although there are analytical solutions that simplify the problem, students and tutors must use efficient numerical strategies to appropriately solve some LFR problems. In the present investigation, we solve several different cases of two‐dimensional cylindrical LFR, beginning with the isothermal case, as a benchmark. Calculations were performed using the Chebyshev orthogonal collocation technique by custom scripts in Scilab, Mathematica©, and Matlab®, and were compared with solutions available from the ECRE Version of COMSOL®, which was used as the reference, as well as available analytical solutions in the literature. After analyzing the case of the isothermal LFR with Newtonian fluid, we explored non‐Newtonian fluids, including Carreau and Bingham fluids, whose LFR results are not available in the preceding literature. For Newtonian fluids, besides (a) the isothermal case, we also explore other three nonisothermal design cases: (b) cooling jacket with a fixed heat exchange coefficient at the wall, (c) adiabatic operation, (d) nonisothermal LFR with isothermal wall. Through a performance criterion, the different operation models are compared and we show that nonisothermal design cases perform better than the isothermal case. Computation times, for different scenarios, are quite short and taking 25 nodal points or more suffice for an accurate and timely solution of the more complex problem Case (b). The numerical modeling approach is useful from a pedagogical standpoint, as one can compare numerical results with classical assumptions, and progress from a more restrictive conceptual model (segregated flow) to an array of different kinds of operation with the LFR in which one needs to consider diffusion and temperature distribution.

Dynamic Optimization of a Supply Chain Operation Model with Multiple Products

Determination of the optimal operational policy for an automotive supply chain is explored under a centralized management approach using dynamic programming. A deterministic optimal control model is proposed to meet multi-product demand over a period while minimizing a cost performance index for a five-echelon network. The production-inventory levels are the state variables and the raw material acquisition rates are the control variables to be decided in the problem. The novelties include parts mixing operations, assembly requirements, and a push–pull chain operation strategy. The continuous model is solved using Iterative Dynamic Programming, an algorithm with successful applications in chemical engineering problems. Its implementation here is the first in supply chain (SC) management models. The results demonstrate that the proposal is suitable to represent the dynamic behavior of the SC and provides useful information to outline a cooperative decision-making process. Managerial insights are derived to improve the resilience and efficiency of the chain.

Crystallisation: Solving crystal nucleation problem in the chemical engineering classroom based on the research grade experiments deployed in virtual mode

Crystallization via nucleation can isolate active pharmaceutical ingredients from their crudes. While chemical engineering textbooks provide theoretical knowledge on crystallization and nucleation theories, they often fall short in providing provide practical insights on the nucleation mechanism. To bridge this gap, we introduced a virtual experiment on nucleation in second-year chemical engineering classrooms. The main goal is to educate students on crystallization procedures in research and process industries, teaching them how to analyse and manage collected data while integrating theoretical knowledge. This includes conveying the kind of information that can be obtained from a crystallisation process and instructing students on how to analyse and manage the data collected in the light of the theories learned. We devised an original chemical engineering problem on nucleation, derived directly from the raw data collected in the classroom from virtual experiments. This method differs from the conventional approach of solving standard textbook problems. The textbook problems, regrettably often lack crucial information on how nucleation rate or surface free energy are directly obtained from raw data. By the conclusion of the virtual experiment, students have acquired a comprehensive understanding encompassing both practical and theoretical aspects of crystallization, with a particular focus on nucleation. The methodologies elucidated in this study can be applied across a spectrum of chemical engineering modules, including process engineering, unit operations in chemical engineering, mass transfer, and can even be integrated into specialized courses dedicated to crystallization.

A Case Study on Enhancing Lithium Ion Battery Safety via Curriculum Reform on Integrating Innovation in Safety Engineering Education

This dissertation investigates two main topics: enhancing the safety of lithium-ion batteries by using ethoxy(pentafluoro)cyclotriphosphazene (PFPN) additives, and implementing educational reforms in safety science and engineering curricula to integrate these state-of-the-art safety approaches. With the increasing demand for lithium-ion batteries, there is a corresponding need for enhanced safety measures to mitigate potential hazards such as thermal runaway and explosions. This research uses differential scanning calorimetry and adiabatic acceleration calorimetry to conduct thorough thermal stability evaluations. The primary objective is to improve comprehension of battery safety and also provide a significant example for educational reform. The text advocates for a revised curriculum that prioritizes practical, safety-focused instruction and experiential learning that mirrors actual chemical engineering problems in the real world. The suggested educational reforms seek to provide students with a comprehensive comprehension of both theoretical and practical aspects of safety Science and Engineering, hence enhancing their readiness for the intricacies of contemporary battery technologies. This dissertation presents a plan for incorporating practical safety solutions into chemical engineering education, demonstrating the advantages of combining academic progress with improvements in instructional methods.

Integrated Mass and Energy Balance Course Project using Spreadsheet for a Chemical Engineering Degree Program

In this innovation, team projects from two courses offered in different semesters are integrated. On top of that, students are trained to use a spreadsheet-based tool to solve the problem in the project. Those two courses are Mass Balance (SETK 1123) and Energy Balance (SETK 2133) offered to Chemical Engineering students in Universiti Teknologi Malaysia at their second and third semester. The main objective of the innovation is to scaffold the students’ understanding of the problems given to them in the projects. At the end of the project, they should be able to clearly see the overall processes in a typical chemical industry, starting from raw materials to the targeted products, by-products, and wastes produced. In addition to that, the projects also foster digital skills by enhancing the students’ skills to use the spreadsheet tools to solve Chemical Engineering problems. This is inline with their study as the students learnt about computer programming and spreadsheet tools in their first semester. From the students’ reflections, it can be concluded that this innovation are able to increase the comprehension of the students on the two courses, increase the skills in using spreadsheet tools for solving problems and also inculcate critical thinking and problem solving skills.

Adaptive Latin Hypercube Sampling for a Surrogate-Based Optimization with Artificial Neural Network

A significant number of sample points are often required for surrogate-based optimization when utilizing process simulations to cover the entire system space. This necessity is particularly pronounced in complex simulations or high-dimensional physical experiments, where a large number of sample points is essential. In this study, we have developed an adaptive Latin hypercube sampling (LHS) method that generates additional sample points from areas with the highest output deviations to optimize the required number of samples. The surrogate model used for the optimization problem is artificial neural networks (ANNs). The standard for measuring solution accuracy is the percent error of the optimal solution. The outcomes of the proposed algorithm were compared to those of random sampling for validation. As case studies, we chose three different chemical processes to illustrate problems of varying complexity and numbers of variables. The findings indicate that for all case studies, the proposed LHS optimization algorithm required fewer sample points than random sampling to achieve optimal solutions of similar quality. To extend the application of this methodology, we recommend further applying it to fields beyond chemical engineering and higher-dimensional problems.

An artificial intelligence course for chemical engineers

Artificial intelligence and machine learning are revolutionising fields of science and engineering. In recent years, process engineering has widely benefited from this novel modelling and optimisation approach. The open literature can offer several examples of their applications to chemical engineering problems. Increasing investments are devoted to these techniques from different industrial areas, but insufficient information on a structured course covering these topics in a chemical engineering curriculum could be found. The course in this paper intends to reduce this gap. We introduce one of the first courses on artificial intelligence applications in a chemical engineering curriculum. The course targets Master's students with a chemical engineering background and insufficient knowledge of statistical approaches. It covers the main aspects by utilising frontal lectures and hands-on exercises with active learning methods. This paper shows the methodology we adapted to introduce students to machine learning techniques and how they responded to each class. The student performances for each test are shown, as well as the survey results based on student feedback and suggestions. This work contains essential guidelines for educators who will provide an artificial intelligence course in a chemical engineering curriculum.

Parameter estimation in non-linear chemical processes: an opposite point-based differential evolution (OPDE) approach

Abstract In recent years, evolutionary algorithms have been gaining popularity for finding optimal solutions to non-linear multimodal problems encountered in many engineering disciplines. Differential evolution (DE), an evolutionary algorithm, is a novel optimization method capable of handling nondifferentiable, non-linear, and multimodal objective functions. DE is an efficient, effective, and robust evolutionary optimization method. Still, DE takes large computational time to optimize the computationally expensive objective functions. Therefore, an attempt to speed up DE is considered necessary. This paper introduces a modification to the original DE that enhances the convergence rate without compromising solution quality. The proposed opposite point-based differential evolution (OPDE) algorithm utilizes opposite point-based population initialization, in addition to random initialization. Such an improvement reduces computational effort. The OPDE has been applied to benchmark test functions and high-dimensional non-linear chemical engineering problems. The proposed method of population initialization accelerates the convergence speed of DE, as indicated by the results obtained using benchmark test functions and non-linear chemical engineering problems.

Efficient Families of Multi-Point Iterative Methods and Their Self-Acceleration with Memory for Solving Nonlinear Equations

In this paper, we have constructed new families of derivative-free three- and four-parametric methods with and without memory for finding the roots of nonlinear equations. Error analysis verifies that the without-memory methods are optimal as per Kung–Traub’s conjecture, with orders of convergence of 4 and 8, respectively. To further enhance their convergence capabilities, the with-memory methods incorporate accelerating parameters, elevating their convergence orders to 7.5311 and 15.5156, respectively, without introducing extra function evaluations. As such, they exhibit exceptional efficiency indices of 1.9601 and 1.9847, respectively, nearing the maximum efficiency index of 2. The convergence domains are also analysed using the basins of attraction, which exhibit symmetrical patterns and shed light on the fascinating interplay between symmetry, dynamic behaviour, the number of diverging points, and efficient root-finding methods for nonlinear equations. Numerical experiments and comparison with existing methods are carried out on some nonlinear functions, including real-world chemical engineering problems, to demonstrate the effectiveness of the new proposed methods and confirm the theoretical results. Notably, our numerical experiments reveal that the proposed methods outperform their existing counterparts, offering superior precision in computation.

Implementing Newton-Raphson Method Algorithm in 6 Programming Languages’ Solution to Van Der Waals Equation of State

Newton’s method can be implemented for the Van der Waals equation of state (VDW EOS) and its derivatives. Application of programming language in chemical engineering is not new but however limited in practice, as it is not included in the curriculum of students of tertiary institutions across the globe. Finding roots through iteration for the VDW EOS can be described as interesting and simple using C++, FORTRAN, R, MATLAB, Java and Python languages; even though a perfect programming language does not exist. In this work, the coding syntax in the six languages employed to solve parameters in VDW EOS differs in terms of simplicity and user-friendliness. Notwithstanding, all of them proves to be an adaptable software, capable of solving chemical engineering problems leading to nonlinear relations

Exploring the use of large language models (LLMs) in chemical engineering education: Building core course problem models with Chat-GPT

This study highlights the potential benefits of integrating Large Language Models (LLMs) into chemical engineering education. In this study, Chat-GPT, a user-friendly LLM, is used as a problem-solving tool. Chemical engineering education has traditionally focused on fundamental knowledge in the classroom with limited opportunities for hands-on problem-solving. To address this issue, our study proposes an LLMs-assisted problem-solving procedure. This approach promotes critical thinking, enhances problem-solving abilities, and facilitates a deeper understanding of core subjects. Furthermore, incorporating programming into chemical engineering education prepares students with vital Industry 4.0 skills for contemporary industrial practices. During our experimental lecture, we introduced a simple example of building a model to calculate steam turbine cycle efficiency, and assigned projects to students for exploring the possible use of LLMs in solving various aspect of chemical engineering problems. Although it received mixed feedback from students, it was found to be an accessible and practical tool for improving problem-solving efficiency. Analyzing the student projects, we identified five common difficulties and misconceptions and provided helpful suggestions for overcoming them. Our course has limitations regarding using advanced tools and addressing complex problems. We further provide two additional examples to better demonstrate how to integrate LLMs into core courses. We emphasize the importance of universities, professors, and students actively embracing and utilizing LLMs as tools for chemical engineering education. Students must develop critical thinking skills and a thorough understanding of the principles behind LLMs, taking responsibility for their use and creations. This study provides valuable insights for enhancing chemical engineering education's learning experience and outcomes by integrating LLMs.

An immersive virtual reality learning environment with CFD simulations: Unveiling the Virtual Garage concept.

Virtual reality has become a significant asset to diversify the existing toolkit supporting engineering education and training. The cognitive and behavioral advantages of virtual reality (VR) can help lecturers reduce entry barriers to concepts that students struggle with. Computational fluid dynamics (CFD) simulations are imperative tools intensively utilized in the design and analysis of chemical engineering problems. Although CFD simulation tools can be directly applied in engineering education, they bring several challenges in the implementation and operation for both students and lecturers. In this study, we develop the "Virtual Garage" as a task-centered educational VR application with CFD simulations to tackle these challenges. The Virtual Garage is composed of a holistic immersive virtual reality experience to educate students with a real-life engineering problem solved by CFD simulation data. The prototype is tested by graduate students (n = 24) assessing usability, user experience, task load and simulator sickness via standardized questionnaires together with self-reported metrics and a semi-structured interview. Results show that the Virtual Garage is well-received by participants. We identify features that can further leverage the quality of the VR experience with CFD simulations. Implications are incorporated throughout the study to provide practical guidance for developers and practitioners.

Solution of Chemical Engineering Models and Their Dynamics Using a New Three-Step Derivative Free Optimal Method

The aim of this research article is to develop a three-step optimal iterative technique using Hermite interpolation for the solution of nonlinear algebraic and transcendental equation arises in chemical engineering models. In this connection, we proposed an optimal three-step eight-order technique without derivative and, has a high efficiency index. The convergence analysis of the proposed method is also discussed. For this demonstration, we apply the new technique to certain nonlinear problems in chemical engineering, such as, the conversion in a chemical reactor, a chemical equilibrium problem, azeotropic point of a binary solution and Continuous Stirred Tank Reactor (CSTR). And the study of dynamics is also used to demonstrate the performance of the presented scheme. It’s observed from the Comparison tables and dynamics, the proposed technique is more efficient compared to other existing methods.

A Review on Artificial Intelligence Enabled Design, Synthesis, and Process Optimization of Chemical Products for Industry 4.0

With the development of Industry 4.0, artificial intelligence (AI) is gaining increasing attention for its performance in solving particularly complex problems in industrial chemistry and chemical engineering. Therefore, this review provides an overview of the application of AI techniques, in particular machine learning, in chemical design, synthesis, and process optimization over the past years. In this review, the focus is on the application of AI for structure-function relationship analysis, synthetic route planning, and automated synthesis. Finally, we discuss the challenges and future of AI in making chemical products.

Numerical Solution of Fractional Boundary Value Problem in Chemical Engineering using the Shooting Method

Numerical Solution of Fractional Boundary Value Problem in Chemical Engineering using the Shooting Method

Chemical Engineering Numerical Analysis with R: Peng-Robinson Equation of State

Likely, many text on MATLAB, C++, FORTRAN and Python programming languages exist in chemical engineering libraries, discussing their applications for chemical engineering numerical analysis. R programming language, which has been in existence for more than 40 years is just evolving as a language of choice for data analytics in science and engineering. Here, it is shown that, numerical analysis with equations of state (EOS), especially the Peng-Robinson EOS, typically taught in undergraduate chemical engineering introductory courses can be solved with a developed or existing R source codes. Out of several other mathematical methods, including Fixed-point iteration, Regula-Falsi, Bisection and their modified/hybrid methods recently developed, only Secant and Newton’s method algorithm were followed to solve a sample problem by writing an R program. Although sufficient, in-depth study of the R language using some recommended manuals in this work can be a guide in implementing a solution with R for other numerical methods, for the same problem, as well as several other existing analytical and statistical chemical engineering problems out there.

Energetic, economic and environmental (3E) optimization of hydrogen production process from coal-biomass co-gasification based on a novel method of Ordering Preference Targeting at Bi-Ideal Average Solutions (OPTBIAS)

The coal-biomass co-gasification process is environmentally friendly for hydrogen production, yet came by the expense of high cost, hence an appropriate trade-off among the conflicting objectives is pursued. Considering the inconsistent ordering or heavy computational burden for the existing multi-criteria decision-making methods, a novel method of Ordering Preference Targeting at Bi-Ideal Average Solutions (OPTBIAS) is proposed. In this method, the weighted information of the top and bottom third ideal points and the average solution, as well as their relative importance are considered to evaluate the performance of each Pareto-optimal solution. The OPTBIAS is tested on the hydrogen production and the benchmark chemical engineering problems. The results prove that compared with classical methods, OPTBIAS has superiority in simplicity, weight sensitivity and consistency robustness. Moreover, OPTBIAS gives consistent ordering results when utilizing alternative objectives (exergy efficiency VS exergy destruction) with the same physical significance, showing good applicability in the real world.

An optimal eighth order derivative free multiple root finding numerical method and applications to chemistry

In this paper, we present an optimal eighth order derivative-free family of methods for multiple roots which is based on the first order divided difference and weight functions. This iterative method is a three step method with the first step as Traub–Steffensen iteration and the next two taken as Traub–Steffensen-like iteration with four functional evaluations per iteration. We compare our proposed method with the recent derivative-free methods using some chemical engineering problems modelled as nonlinear equations with simple and multiple roots. Stability of the presented family of methods is demonstrated by using the graphical tool known as basins of attraction.

From Amundson, Aris, and Sargent to the future of process systems engineering

In this paper, we attempt to provide a perspective on the field of process systems engineering (PSE) by tracing its evolution back to the pioneering work of Neal Amundson, Rutherford Aris, and Roger Sargent, and highlighting their legacies that continue to guide research in PSE to this day. We underscore the growth in the technical scope of the field from the adoption of control and optimization methods for analyzing chemical engineering problems to the advancement of the methods themselves. We comment on the extended scope, current state, and major trends in PSE while reflecting on the intellectual identity of our field that has developed over time. PSE is positioned to play a crucial role in addressing major societal challenges, such as sustainability and health. While considering a diverse set of applications, it will also be critical to further advance theory which allows us to address the complexity that underlies these problems. Finally, we outline a few emerging research themes that could serve as initial food for thought in a broader discussion on future research directions in PSE.

A Hybrid Competitive Evolutionary Neural Network Optimization Algorithm for a Regression Problem in Chemical Engineering

Neural networks have demonstrated their usefulness for solving complex regression problems in circumstances where alternative methods do not provide satisfactory results. Finding a good neural network model is a time-consuming task that involves searching through a complex multidimensional hyperparameter and weight space in order to find the values that provide optimal convergence. We propose a novel neural network optimizer that leverages the advantages of both an improved evolutionary competitive algorithm and gradient-based backpropagation. The method consists of a modified, hybrid variant of the Imperialist Competitive Algorithm (ICA). We analyze multiple strategies for initialization, assimilation, revolution, and competition, in order to find the combination of ICA steps that provides optimal convergence and enhance the algorithm by incorporating a backpropagation step in the ICA loop, which, together with a self-adaptive hyperparameter adjustment strategy, significantly improves on the original algorithm. The resulting hybrid method is used to optimize a neural network to solve a complex problem in the field of chemical engineering: the synthesis and swelling behavior of the semi- and interpenetrated multicomponent crosslinked structures of hydrogels, with the goal of predicting the yield in a crosslinked polymer and the swelling degree based on several reaction-related input parameters. We show that our approach has better performance than other biologically inspired optimization algorithms and generates regression models capable of making predictions that are better correlated with the desired outputs.