Chemical Engineering Research & Design最新文献

{"title":"Critical theoretical and thermodynamic comments on the paper \"Enhanced adsorption of methyl orange using polyaniline-modified hydroxyapatite derived from natural Moroccan phosphate; https://doi.org/10.1016/j.cherd.2026.01.051\"","authors":"Éder C. Lima,&nbsp;Moaaz K. Seliem,&nbsp;Glaydson S. dos Reis","doi":"10.1016/j.cherd.2026.06.037","DOIUrl":"10.1016/j.cherd.2026.06.037","url":null,"abstract":"<div><div>This commentary highlights key theoretical, statistical, and thermodynamic errors in the recently published article by Benchhiba et al., 2026, concerning the adsorption of methyl orange onto polyaniline-modified hydroxyapatite. We highlight fundamental misapplications in the mathematical evaluation of adsorption kinetics, equilibrium isotherms, and thermodynamics. Specifically, the original study relies on the statistical fallacy of linearizing inherently nonlinear adsorption models, which distorts error structures and invalidates R² comparisons. Furthermore, this commentary exposes severe dimensional inconsistencies in the calculation of the Dubinin-Radushkevich, Temkin, and Freundlich isotherms. Most critically, the derivation of standard thermodynamic parameters (ΔG°, ΔH°, ΔS°) is fundamentally flawed due to the erroneous use of a dimensional distribution coefficient (K_d) instead of a dimensionless thermodynamic equilibrium constant (<span><math><msubsup><mrow><mi>K</mi></mrow><mrow><mi>e</mi></mrow><mrow><mn>0</mn></mrow></msubsup></math></span>), leading to extreme physical contradictions regarding the nature of adsorption (physisorption vs. chemisorption). By elucidating the correct mathematical and dimensional frameworks, this commentary aims to rectify the scientific record.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 554-562"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-driven prediction of drug-likeness for peptides: An evaluation of newly created MLP and k-NN models","authors":"Momchil Lambev,&nbsp;Dimana Dimitrova,&nbsp;Plamen Bekyarov,&nbsp;Silviya Mihaylova","doi":"10.1016/j.cherd.2026.06.034","DOIUrl":"10.1016/j.cherd.2026.06.034","url":null,"abstract":"<div><div>Peptide-based drug candidates frequently fall outside the boundaries of classical drug-likeness rules due to their high molecular weight, polarity, and structural flexibility. The aim of this study was to develop and evaluate machine learning (ML) models based on multilayer perceptron (MLP) and k-nearest neighbors (k-NN) algorithms for predicting violations of Lipinski’s Rule of Five (Ro5), beyond Rule of Five (bRo5), and the Muegge rule for structurally diverse peptide molecules.</div><div>ML models were trained using physicochemical data from PubChem and applied to a data set of 26 tested peptide molecules. Predicted violations counts were compared with SwissADME, Molinspiration platforms, and manually calculated bRo5 values.</div><div>MLP and k-NN models demonstrated excellent predictive performance, with accuracy, precision, recall, and F1-score values ranging from 0.98 to 0.99 for Ro5 and bRo5, and remaining above 0.94 for the Muegge rule. Prediction errors were limited to ±1 violation and occurred mainly for borderline structures near rule thresholds. The k-NN model exhibited higher consistency for bRo5 predictions, while MLP showed greater stability for Ro5.</div><div>Descriptor-based ML models can reliably capture drug-likeness trends within peptide chemical space. The developed models complement ensemble approaches such as RF and represent efficient <em>in silico</em> tools for early assessment of peptide drug candidates.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 693-702"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148453259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Bayesian parameter calibration for DEM simulations of shear cell test","authors":"Giovanni Iozzi,&nbsp;Francesca O. Alfano,&nbsp;Sina Zinatlou Ajabshir,&nbsp;Diego Barletta,&nbsp;Massimo Poletto,&nbsp;Francesco P. Di Maio,&nbsp;Alberto Di Renzo","doi":"10.1016/j.cherd.2026.06.017","DOIUrl":"10.1016/j.cherd.2026.06.017","url":null,"abstract":"<div><div>Shear test devices are commonly employed to characterise the mechanical behaviour of (cohesive) particulate materials. In simulations, Discrete Element Method (DEM) models require correct material parameter values for accurate predictions in a broad variety of process applications. In the present work, two open-source codes implementing robust Bayesian calibration (GrainLearning) and DEM (MFIX-DEM) are effectively integrated into a framework to investigate the capability of DEM to represent the key features of experimental shear cell test data on a model granular material. Material properties, geometric characteristics and operating procedures were carefully matched by using a periodic box subjected to stress- and displacement-controlled walls. Properties such as the static friction coefficient, rolling friction coefficient, and cohesive energy in DEM are varied in a statistically consistent way to capture the experimental data of the yield loci collected on spherical glass beads. With the calibration process for standard DEM, it was found that, under the conditions investigated, no combination of reasonable parameter values yielded sufficient shear resistance. So, in analogy with approaches documented in the literature, spherical particles with partially and fully locked rotation have been considered, with only the latter case being successful. The sequential Bayesian filtering approach allowed appropriate DEM parameters to be found along with their uncertainty quantification. Using these values, various other experimental features were captured with good agreement, including the evolution of the shear stress during loading and the dilation behaviour. Finally, DEM results were processed to examine the formation of the shear displacement vertical profile from the elastic to the yielding stage.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 343-360"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458498","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"QUBO formulation of process superstructures via extended process network theory: Opportunities and current limitations","authors":"Kazuki Fukushima,&nbsp;Hajime Ohno,&nbsp;Yasuhiro Fukushima","doi":"10.1016/j.cherd.2026.06.032","DOIUrl":"10.1016/j.cherd.2026.06.032","url":null,"abstract":"<div><div>Superstructure synthesis enables comprehensive process optimization but requires efficient methods to solve large-scale combinatorial problems. This study develops an extended process network theory that formulates process superstructures as quadratic unconstrained binary optimization (QUBO) problems, enabling the use of emerging combinatorial optimization solvers, including quantum annealers. By introducing binary variables into the adjacency matrix and developing polynomial approximations for recycling stream inverse matrices and fractional concentration terms, the proposed methodology extends process network theory from fixed-structure analysis to superstructure optimization. The approach is demonstrated through a two-stage separation case study with a recycling configuration. Computational experiments compare three solver types: a commercial optimizer (Gurobi), a graphics processing unit (GPU)-based QUBO solver, and quantum annealing hardware (D-Wave Advantage2). The GPU solver maintains stable performance across all problem scales, with solution times of 3–4 s, whereas the commercial optimizer exhibits exponential time growth at higher discretization levels. Current quantum annealing hardware successfully solves minimal instances but fails at larger scales owing to physical topology connectivity constraints rather than qubit count limitations. The variable growth resulting from quadratization remains a significant challenge. These results establish a theoretical framework for QUBO-based process optimization. Practical implementation will depend on advances in quantum hardware connectivity and hybrid decomposition strategies.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 563-575"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced performance of conical-helical coil condenser under laminar condition: Effect of secondary flow on heat extraction","authors":"Vandana Kumari Jha,&nbsp;Soubhik Kumar Bhaumik","doi":"10.1016/j.cherd.2026.06.041","DOIUrl":"10.1016/j.cherd.2026.06.041","url":null,"abstract":"<div><div>Conical helical coil (CHC) configurations remain relatively less explored, despite their notable implications in flow physics and allied transport. A key feature of this geometry is the shrinking helix radius, which progressively intensifies secondary flow, thereby compensating for the decreasing temperature gradient along the flow path. This work presents a comprehensive CFD-based numerical investigation of flow and heat transfer in a CHC condenser with constant pitch, comparing its performance against an equivalent simple helical coil (SHC) geometry having the same total tube length. The flow regime in all simulated cases remains laminar, with Reynolds numbers <span><math><mrow><mo>(</mo><mrow><mn>58</mn><mo>≤</mo><mi>Re</mi><mo>≤</mo><mn>10068</mn></mrow><mo>)</mo></mrow></math></span> consistently below the critical transition criterion. CHC configurations for cone angles <em>(</em>2<em>ϕ</em>): 10°, 20°, 30° and 38° were deduced geometrically. The analysis includes characterization of: (i) axial and secondary flow patterns within the tube, (ii) temperature evolution along the flow path and corresponding heat extraction performance on a turn-by-turn and overall basis, (iii) the Nusselt number (<em>Nu</em>) on a turn-by-turn and overall basis, and (iv) pressure drop characteristics. Results reveal intensifying secondary flow with increasing cone angle, which is reflected through a sharper rise in coolant temperature along the tube-flow-path, leading to higher exit temperatures compared to SHC. Numerical predictions of heat extraction show an improved performance of CHC with an increase in cone angle, reaching up to 14.63% for <span><math><mrow><mn>2</mn><mi>ϕ</mi><mo>=</mo><mn>38</mn><mo>°</mo></mrow></math></span>. <em>Nu</em> results reveal progressively higher values along the flow path, exceeding ∼29% of SHC, thereby proving instrumental in substantially enhancing the thermal performance. Thermodynamic analysis reveals that CHCs show a slight increase in entropy generation (up to 5.85% for CHC_38), it remains thermodynamically negligible, indicating no significant second-law penalty for the enhanced heat transfer. CHC configurations exhibit a moderate increase in pressure drop values compared to SHC, which can be considered an acceptable trade-off for the observed gains in heat transfer efficiency.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 620-636"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Regulating bubble specific surface area and pressure drop in microchannels via embedded obstacles","authors":"Dandan Zhang,&nbsp;Rujie Wang,&nbsp;Weifang Niu,&nbsp;Wenyuan Fan,&nbsp;Taotao Fu,&nbsp;Meitong Li","doi":"10.1016/j.cherd.2026.06.026","DOIUrl":"10.1016/j.cherd.2026.06.026","url":null,"abstract":"<div><div>Gas-liquid flow hydrodynamics are essential for controlling multiphase transport processes and reaction properties in the microreactors. In this work, gas-liquid two-phase flow is regulated effectively by arranging obstacle array in microchannels using a coupled Level Set and Volume of Fluid (CLSVOF) method. The numerical model is validated against experimental data, confirming its reliability for capturing interface dynamics. Through analysis of Nitrogen bubble evolution, the flow regime during bubble-obstacle interaction is clarified, and five distinct transition stages are identified. The effects of key parameters, including liquid velocity, glycerol concentration, surfactant concentration, obstacle spacing, and gas-liquid velocity ratio, on the average bubble specific surface area (SSA) and system pressure drop are systematically quantified. The results show that SSA and pressure drop increase with liquid velocity, glycerol concentration, and surfactant concentration, but decrease with obstacle spacing. The most significant enhancement occurs in 60% glycerol solutions. Increasing the gas-liquid velocity ratio raises pressure drop while reducing SSA, with both trends saturating at higher ratios. Based on the systematic investigation of various factors, a novel and more accurate pressure-drop correlation for both straight and obstacle-laden microchannels is developed, which outperforms existing models when tested against literature data.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 361-375"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and characterization of density distributions for binary dense medium in gas–solid separation fluidized beds","authors":"Song Wang,&nbsp;Yanhong Fu,&nbsp;Yandaizi Zhou,&nbsp;Liang Dong,&nbsp;Tielin Wang,&nbsp;Dongsheng He","doi":"10.1016/j.cherd.2026.06.025","DOIUrl":"10.1016/j.cherd.2026.06.025","url":null,"abstract":"<div><div>This study experimentally investigates and models the fluidization and density distribution characteristics of binary dense medium systems composed of magnetite and coal powders in air dense medium fluidized beds (ADMFBs). Three types of binary systems, namely Geldart B-B, B-A, and B-C, were examined under various superficial gas velocities (<em>N</em> = 1.2–1.8) to analyze axial and radial bed density distributions, density fluctuations, and the effects of fine-particle fractions. For the B-B system, density uniformity is limited due to weak segregation and small particle size differences, and bed density tends to increase with fine-particle fraction. The B-A system exhibits strong axial segregation, and although fine particles reduce density fluctuation intensity, excessive fines can be elutriated, limiting density range adjustment. The B-C system demonstrates an optimal balance: moderate fine-particle fractions (<em>W</em>_<em>f</em> = 6–10%) produce a relatively uniform bed-density distribution with low fluctuation intensity, lowering the density limit while maintaining low fluctuations. A correlation for predicting the average bed density of binary dense medium systems was developed based on particle size ratio and fine-particle fraction, with prediction errors within ±12% for <em>W</em>_<em>f</em> &lt; 15%. The study highlights that particle size, density differences, and gas velocity critically influence bed fluidization, bed-density fluctuation behavior, and the adjustable density range, providing guidance for optimizing binary dense media in ADMFB coal beneficiation.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 293-306"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148458496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyanobacterial biological processes: A physics-informed neural network approach to biochemical kinetics","authors":"Miriam Nnadili,&nbsp;Jose A. Romagnoli,&nbsp;Michael G. Benton","doi":"10.1016/j.cherd.2026.06.055","DOIUrl":"10.1016/j.cherd.2026.06.055","url":null,"abstract":"<div><div>Cyanobacteria-phycocyanin (C-PC) is a valuable bio-pigment widely used in the food and pharmaceutical industries, but its production in bioreactors faces significant challenges, primarily due to low biomass concentration and productivity. Unlike previous ANN-based fed-batch optimization studies that relied primarily on data-driven learning, this study develops a physics-informed reinforcement learning framework that integrates biochemical kinetics, Physics-Informed Neural Networks (PINNs), and Proximal Policy Optimization (PPO) to model and optimize C-PC production in cyanobacterial bioreactors. The proposed PINN embeds Monod-type biochemical growth kinetics directly into the neural network training process through ODE-constrained loss functions, enabling biologically consistent predictions and improved extrapolation capability under sparse experimental conditions. In parallel, the PPO-based reinforcement learning agent optimizes nitrate dosing strategies through a reward-driven control framework. The resulting control strategy prioritizes early-phase nitrate availability, aligning with established cyanobacterial growth dynamics. Simulations demonstrate that the PPO-guided optimization approach enhances nitrate utilization efficiency and improves predicted C-PC production relative to conventional fixed or stepwise nitrate-addition methods under evaluated model conditions. This study highlights the potential benefits of integrating machine learning-driven process control with physics-informed modeling for adaptive cyanobacterial bioprocess optimization.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 774-786"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental flow field studies on MIG and INTERMIG impellers under varying fluid viscosity and baffle number using stereo particle image velocimetry","authors":"Lena Kögel,&nbsp;Felix Müther,&nbsp;Jona Schüßler,&nbsp;Mathias Ulbricht,&nbsp;Heyko Jürgen Schultz","doi":"10.1016/j.cherd.2026.06.039","DOIUrl":"10.1016/j.cherd.2026.06.039","url":null,"abstract":"<div><div>MIG (multi-stage impulse counterflow) and INTERMIG (interference multi-stage impulse counterflow) impellers are considered highly effective for various mixing tasks and are widely used in industrial processes. However, experimental data on their flow characteristics remain scarce, as detailed design information is not publicly available and their special geometry, with high impeller-to-tank ratios and staggered multi-stage arrangement, poses significant challenges for optical measurements. This study presents two novel experimental particle image velocimetry (PIV) approaches that overcome these limitations. Partially transparent impellers allow shadow-free, full-field data acquisition, while a glycerol-based refractive-index-matching fluid ensures distortion-free optical access at varying fluid viscosities. Using these techniques, flow characteristics of MIG and INTERMIG impellers are systematically investigated for the first time in one-, two-, and three-stage configurations, for both pumping directions, varying baffle numbers, and different fluid viscosities. The results reveal pronounced configuration-dependent hydrodynamics, including variations in velocity distribution, stage interaction, surface aeration, bottom and wall velocities, and the relative contributions of axial, radial, and tangential velocity components. This study provides deeper insight into MIG and INTERMIG flow behavior and establishes a comprehensive experimental basis for more precise, configuration-specific guidelines, which enable process-tailored impeller design and are essential to fully exploit their versatility.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 503-516"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452786","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation-based techno-economic optimization of continuous ibuprofen manufacturing under uncertainty","authors":"T. Asrav,&nbsp;M. Alvarado-Morales,&nbsp;G. Sin","doi":"10.1016/j.cherd.2026.06.051","DOIUrl":"10.1016/j.cherd.2026.06.051","url":null,"abstract":"<div><div>A robust techno-economic assessment is essential to evaluate and facilitate the adoption of continuous manufacturing technologies, as well as to guide process development. We present a novel framework for uncertainty-aware techno-economic assessment. This framework combines conceptual process design and simulation, global sensitivity analysis, stochastic optimization, and economic evaluation under uncertainty in a unified workflow. We apply the framework to the continuous manufacturing of ibuprofen, which is predominantly manufactured through established batch processes, and benchmark its performance against a conventional deterministic optimization approach. While the deterministic optimization converges more rapidly to a lower levelized cost of production (LCOP) for the base-case price, the stochastic approach provides a more conservative, robust solution, reflecting real-world process variability. The resulting optimized design remains economically competitive, with an LCOP below current market prices, while ensuring greater reliability under uncertainty. By capturing variability in economic parameters, our approach enhances decision-making and facilitates exploration of conceptual design performance under uncertainty. This comparison highlights the importance of integrating uncertainty quantification into technology shifts and strategic process development.</div></div>","PeriodicalId":10019,"journal":{"name":"Chemical Engineering Research & Design","volume":"231 ","pages":"Pages 637-644"},"PeriodicalIF":4.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148452787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}