TY - JOUR AU - Achuenu, Ifeanyi AU - Lekmang, C. Isah AU - Hyeladi, Dibal AU - Geoffrey, Mica Kumleng TI - Application of partial differential equation (PDE) and integral calculus to rock forming minerals JF - Sigma&Mu: Journal of Mathematics Education, Mathematics, Statistics and Data Science J2 - Sigma&Mu VL - 4 PY - 2026 IS - 1 SP - 11 EP - 23 PG - 13 SN - 2987-2030 UR - https://m2.mtmt.hu/api/publication/37022738 ID - 37022738 LA - English DB - MTMT ER - TY - JOUR AU - Aprile, Giovanni AU - Sagmeister, Peter AU - Devos, Cedric AU - Udepurkar, Aniket AU - Destro, Francesco AU - Molajafari, Fateme AU - Stelzer, Torsten AU - Ismail, Hamza Youssef AU - Griffin, Daniel J. AU - Braatz, Richard D. AU - Myerson, Allan S. TI - Automation in Pharmaceutical Crystallization JF - ORGANIC PROCESS RESEARCH & DEVELOPMENT J2 - ORG PROCESS RES DEV VL - June 25 PY - 2026 IS - & SP - & SN - 1083-6160 DO - 10.1021/acs.oprd.6c00057 UR - https://m2.mtmt.hu/api/publication/37361629 ID - 37361629 LA - English DB - MTMT ER - TY - JOUR AU - Barhate, Yash AU - Kang, Yung Shun AU - Nazemifard, Neda AU - Renner, C. Benjamin AU - Yang, Yihui AU - Papageorgiou, Charles AU - Nagy, Zoltan K. TI - Digital design of crystallization processes using statistical machine learning JF - COMPUTERS & CHEMICAL ENGINEERING J2 - COMPUT CHEM ENG VL - 206 PY - 2026 SN - 0098-1354 DO - 10.1016/j.compchemeng.2025.109518 UR - https://m2.mtmt.hu/api/publication/36492957 ID - 36492957 LA - English DB - MTMT ER - TY - JOUR AU - Bereczki-Horváth, Lili Virág AU - Szilágyi, Botond TI - Automatic Design of Wet-Milling-Integrated Batch Crystallization Processes by Process Analytical Technology-Based Split-Range Direct Nucleation Control JF - ORGANIC PROCESS RESEARCH & DEVELOPMENT J2 - ORG PROCESS RES DEV VL - 30 PY - 2026 IS - 4 SP - 861 EP - 871 PG - 11 SN - 1083-6160 DO - 10.1021/acs.oprd.5c00402 UR - https://m2.mtmt.hu/api/publication/37018173 ID - 37018173 N1 - This research was supported by Hungarian National Scientific Research Fund (OTKA) grant FK-138475. AB - Wet milling is increasingly employed as a process-intensification tool in pharmaceutical batch crystallization, not only as a terminal particle-size reduction step but also as an integrated unit operation synchronized with the crystallizer to enhance process robustness and expand the attainable range of particle-size and shape distributions. Designing a high-performance batch crystallization process is challenging because of the large number of design variables that often require repeated crystallization and dissolution cycles. Synchronizing the crystallizer and wet-milling operations adds further complexity. Direct nucleation control (DNC) is widely used to automatically design cycles of crystal growth and dissolution for single-input, single-output systems─for example, adjusting the temperature to maintain the relative number density within a predefined range. This study extends the DNC principle to handle an integrated system. We propose a sequential split-range controller that regulates the relative number density by employing cooling and heating as the primary manipulated variables. When the target particle count cannot be achieved by cooling alone, a wet-milling cycle is automatically initiated to increase the count through high shear, thereby enhancing the nucleation, attrition, and breakage. Experimental results indicate that this control strategy can drive the process beyond the limits of conventional DNC, producing repeated cycles of growth and nucleation (by cooling or sequential cooling and milling) and dissolution (by heating). In addition to generating larger crystals, the automatic milling cycles also reduced the aspect ratio of rod-like particles, demonstrating the potential for simultaneous crystal-shape control. Repeated implementation of the integrated operation discovered by split-range DNC exhibited high reproducibility, underscoring the potential of the method not only as a control strategy but also as a rapid process-design tool. LA - English DB - MTMT ER - TY - JOUR AU - Budimir Sacher, Josip AU - Bolf, Nenad AU - Rogue, Manon TI - Direct Nucleation Control with External Heating Loop: Process System Engineering, Simulation and Experimental Investigation JF - CRYSTALS J2 - CRYSTALS VL - 16 PY - 2026 IS - 4 SP - 248 SN - 2073-4352 DO - 10.3390/cryst16040248 UR - https://m2.mtmt.hu/api/publication/37074679 ID - 37074679 AB - The aim of this work was to explore and test the concept of a novel direct nucleation control method with external dissolution of fine crystals (E-DNC). It was postulated that the heating cycles of the internal DNC method could be transferred from the crystallizer jacket to an electrically heated recirculation tube, thereby using less energy and requiring a shorter time compared to internal DNC. The conceptual model was explored and developed by reviewing previous research on the topic and addressing known drawbacks. Engineering experience and a heuristic approach led to the conclusion that five commonly used controllers would suffice for a straightforward implementation of the method. A simplified simulation was developed to compare the time and energy requirements for the internal and external DNC methods, and it was concluded that external DNC uses 37% less energy and 19% less time compared to internal DNC. The laboratory system was then constructed by modifying the internal DNC apparatus with inexpensive and commonly used components. A linear cooling experiment was performed to establish the baseline for comparison with E-DNC experiments and to set the expected count range. The E-DNC experiments were then conducted with the aim of obtaining larger crystal sizes, and it was shown that the process could be designed in only three experiments. Adjusting the heating rate and count limit led to a significant increase in median crystal size (22.5%) compared to linear cooling. LA - English DB - MTMT ER - TY - JOUR AU - Deng, Zhicheng AU - Zhang, Ruochen AU - Wang, Jiarui AU - Dong, Jiayu AU - Wang, Simin TI - Hydrodynamics–kinetics coupling in stirred-tank para-xylene crystallization: Roles of flow pattern and vortex structure JF - CHEMICAL ENGINEERING RESEARCH AND DESIGN J2 - CHEM ENG RES DES VL - 234 PY - 2026 SP - 68 EP - 81 PG - 14 SN - 0263-8762 DO - 10.1016/j.cherd.2026.08.054 UR - https://m2.mtmt.hu/api/publication/37505573 ID - 37505573 LA - English DB - MTMT ER - TY - JOUR AU - Liu, Weiqi AU - Wei, Guixuan AU - Zhao, Wang AU - Luo, Wei AU - Tong, Lu AU - Jiang, Yanbin TI - Modified method for efficient in-situ fines elimination during crystallization of disodium 5′-ribonucleotide using controllable jet feeding and particle sedimentation JF - POWDER TECHNOLOGY J2 - POWDER TECHNOL VL - 480 PY - 2026 SN - 0032-5910 DO - 10.1016/j.powtec.2026.122699 UR - https://m2.mtmt.hu/api/publication/37311340 ID - 37311340 LA - English DB - MTMT ER - TY - JOUR AU - Ma, Yiming AU - Yuan, Xuming AU - Yewale, Ashish AU - Benyahia, Brahim TI - Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies JF - PARTICUOLOGY J2 - PARTICUOLOGY VL - 110 PY - 2026 SP - 223 EP - 253 PG - 31 SN - 1674-2001 DO - 10.1016/j.partic.2026.01.014 UR - https://m2.mtmt.hu/api/publication/36952655 ID - 36952655 N1 - Funding Agency and Grant Number: UKRI Horizon Europe Guarantee scheme [HLTH-2021-IND-07, 101057430] Funding text: This project has received funding under the call HORIZON- HLTH-2021-IND-07 (SusPharma, grant agreement no. 101057430) and under the UKRI Horizon Europe Guarantee scheme (SusPharma, project reference no. 10038378) . AB - Crystallization plays a critical role across multiple industries, determining key particulate product attributes such as purity, particle size distribution, morphology, and polymorphic form. The multiscale nature of this process, encompassing molecular interactions, phase transitions, and transport phenomena, imposes high levels complexity on the design and control of systems in which particle characteristics govern performance. Digital strategies, including emerging AI-driven approaches, are increasingly recognized as powerful tools for managing multiscale complexity, reducing inherent uncertainties, and enhancing process development. This review aims to explore recent advances and critically analyze how digital methods can be applied at each stage of process development. The discussion begins with data acquisition and augmentation, including synthetic data generation, model-based experimental design, and rigorous data preprocessing and validation. This is followed by the modeling strategies tailored to specific design and operation objectives, including mechanistic, data-driven, and hybrid approaches for predicting crystallization dynamics and particulate properties. Finally, recent control and optimization solutions are discussed, focusing on model-based and adaptive algorithms for open and closed-loop strategies. The review concludes with a forward-looking perspective on emerging trends, highlighting the integration of digital twins, real-time optimization, and sustainability metrics which together are expected to enable intelligent, resilient, and sustainability-aligned crystallization systems capable of meeting future indus-trial and regulatory requirements. LA - English DB - MTMT ER - TY - JOUR AU - Wong, Lok Hang AU - Mitchell, Niall Anthony AU - Heng, Jerry Yong Yew TI - Population balance modelling case studies for protein crystallisation: Workflows addressing impurities and heterogeneous seeding JF - CHEMICAL ENGINEERING RESEARCH AND DESIGN J2 - CHEM ENG RES DES VL - 232 PY - 2026 SP - 544 EP - 565 PG - 22 SN - 0263-8762 DO - 10.1016/j.cherd.2026.06.049 UR - https://m2.mtmt.hu/api/publication/37528373 ID - 37528373 AB - In this study, a population balance modelling (PBM) workflow for protein crystallisation is introduced and applied to two case studies, modelling lysozyme crystallisation in the presence of protein impurities (thaumatin and bovine serum albumin (BSA)) and heterogeneous silica seeds. The workflow systematically applies engineering judgement to identify phenomena (nucleation, growth, and enhancement/inhibition by foreign species), incorporates linear and exponential empirical adjustments to traditional kinetic models, and employs sequential kinetic parameter estimation to quantify phenomena. Across the two studies, workflow applicability was demonstrated for different crystalliser configurations (static/orbital shaking), buffer systems, foreign species, and scale-up (1-5 mL), whilst also overcoming experimental and solubility data limitations often encountered in high-cost crystallisation experiments. Although some calibrated parameters were uncertain with the 95% t-value smaller than the reference (attributed to a need for larger datasets), the workflow not only increased system knowledge but also revealed a critical knowledge gap: the complex, protein impurity-dependent behaviour of ternary lysozyme-silica-impurity systems, which traditional kinetic models proved unable to capture. The proposed workflow is broadly applicable to other peptides, proteins, or even small molecules, positioning it as a potential method for accelerating process design and understanding. LA - English DB - MTMT ER - TY - JOUR AU - Alqarni, Mohammed AU - Alqarni, Ali TI - Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization JF - SCIENTIFIC REPORTS J2 - SCI REP VL - 15 PY - 2025 IS - 1 SN - 2045-2322 DO - 10.1038/s41598-024-84741-1 UR - https://m2.mtmt.hu/api/publication/35801953 ID - 35801953 LA - English DB - MTMT ER - TY - JOUR AU - Balogh, László AU - Bódis, Jenő AU - Szilágyi, Botond AU - Bárkányi, Ágnes AU - Egedy, Attila TI - Limited Data based kinetic modeling and optimization of propionic acid synthesis over supported Rh/C catalyst JF - CHEMICAL ENGINEERING JOURNAL ADVANCES J2 - CHEM ENG J ADV VL - 21 PY - 2025 PG - 10 SN - 2666-8211 DO - 10.1016/j.ceja.2024.100693 UR - https://m2.mtmt.hu/api/publication/35631684 ID - 35631684 N1 - This work has been implemented by the TKP2021-NKTA-21 project with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund , financed under the 2021 Thematic Excellence Programme funding scheme. LA - English DB - MTMT ER - TY - JOUR AU - Moshiritabrizi, Iman AU - McMullen, Jonathan P. AU - Wyvratt, Brian M. AU - McAuley, Kimberley B. TI - A Comparative Study of Strategies for Incorporating Uncertainty in Design Space Determination for Pharmaceutical Manufacturing JF - INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH J2 - IND ENG CHEM RES VL - 64 PY - 2025 IS - 45 SP - 21658 EP - 21668 PG - 11 SN - 0888-5885 DO - 10.1021/acs.iecr.5c03037 UR - https://m2.mtmt.hu/api/publication/36601815 ID - 36601815 LA - English DB - MTMT ER - TY - JOUR AU - Orosz, Álmos AU - Neal, Monika AU - Rao, Rekha AU - Roberts, Christine AU - Szilágyi, Botond AU - Nagy, Zoltán K. TI - 2D population balance modeling and ML-based multi-objective optimization for the crystallization process of resveratrol JF - AICHE JOURNAL J2 - AICHE J VL - 71 PY - 2025 IS - 12 PG - 18 SN - 0001-1541 DO - 10.1002/aic.70094 UR - https://m2.mtmt.hu/api/publication/36372919 ID - 36372919 N1 - This work was supported by the Laboratory Directed Research and Development program at Sandia National Laboratories, a multimission laboratory managed and operated by National Technology and Engineering Solutions of Sandia LLC, a wholly owned subsidiary of Honeywell International Inc. for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. Álmos Orosz would like to express his gratitude to the University Research Scholarship Program (EKÖP) for their financial support throughout this research: the project no. IKT-2024-900-I1-00000836/0000008 has been implemented with the support provided by the Ministry of Culture and Innovation of Hungary from the National Research, Development and Innovation Fund, financed under the EKÖP-24-3-BME-29 funding scheme. AB - Crystallization is critical in pharmaceutical manufacturing, influencing active pharmaceutical ingredient (API) purity and processability. This study models the cooling crystallization of resveratrol in a water‐ethanol solvent using a two‐dimensional population balance model (2D‐PBM). Experimental data from Focused Beam Reflectance Measurement (FBRM), UV/Vis spectroscopy, and microscopy supported model calibration via design of experiments. The well‐calibrated model enabled multi‐objective optimization (MOO) to (1) maximize yield and minimize batch time, and (2) explore the relationship between aspect ratio and median crystal size. While the first scenario showed minimal trade‐offs, the second revealed a balance between aspect ratio and size/yield. A hybrid approach combining mechanistic modeling with machine learning drastically accelerated simulations and enabled efficient prediction of Pareto‐optimal solutions. This integration offers a scalable and accurate optimization strategy for complex crystallization processes. LA - English DB - MTMT ER - TY - JOUR AU - Orosz, Álmos AU - Sándor, Levente AU - Firoozirad, Khadijeh AU - Pusztai, Éva AU - Nagy-György, Péter AU - Szilágyi, Botond TI - Streamlining crystallization process scale-up using statistical modeling of experiments and complementary CFD flow fields JF - POWDER TECHNOLOGY J2 - POWDER TECHNOL VL - 458 PY - 2025 PG - 10 SN - 0032-5910 DO - 10.1016/j.powtec.2025.120934 UR - https://m2.mtmt.hu/api/publication/36051983 ID - 36051983 AB - Our paper presents a simple method that bridges computational fluid dynamics (CFD) simulations with systematic experimentation using multivariable statistics, which was developed in the spirit of rapid applicability, quick transferability, and practical simplicity in the process development and scale-up stage. This applies to technologies that have already been developed on a small scale, and it applies when the focal point of scale-up is the stirring conditions, rather than heat transfer or other issues. We propose identifying the predictive CFD variables on the measured CQAs and then training a predictive model that allows us to estimate the CQAs from the simulated flow fields in a technology transfer. The case study of cooling crystallization of L-glutamic acid demonstrates the workflow. Small-scale experiments (0.5 L) are performed with varied active volume and agitation rates, and the particle size distributions (PSD) of the products are measured. The corresponding quasi-steady-state CFD simulations were executed to obtain the flow field using the k−ɛ turbulence model. A partial least squares (PLS)–based recursive feature selection identified the predictive mixing parameters from the plethora of available CFD variables and simultaneously built a predictive model for the product CQA-s. Five critical CFD variables were identified in this case study, all related to the shear-rate distributions. The calibrated optimal PLS model had two components, and allowed the prediction of the mean sizes of the product, that is, Dv10, Dv50, and Dv90 on a 5 L scale with 0.2, 16.4, and 1.2μm deviation from the measured values. This data-driven method simplifies and accelerates the CFD-based scale-up under certain conditions (moderate nonlinearities, flow-centric problem) but does not aim to replace the meticulously identified nonlinear first-principles models. © 2025 The Authors LA - English DB - MTMT ER - TY - JOUR AU - Pessina, Daniele AU - Calderon De Anda, Jorge AU - Heffernan, Claire AU - Heng, Jerry Y. Y. AU - Papathanasiou, Maria M. TI - Integrated In Vitro / In Silico Uncertainty Quantification Method for Protein Crystallization Models JF - INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH J2 - IND ENG CHEM RES VL - 64 PY - 2025 IS - 24 SP - 12025 EP - 12035 PG - 11 SN - 0888-5885 DO - 10.1021/acs.iecr.4c04517 UR - https://m2.mtmt.hu/api/publication/36258769 ID - 36258769 LA - English DB - MTMT ER - TY - CHAP AU - Pessina, Daniele AU - Anda, Jorge Calderon de AU - Heffernan, Claire AU - Tian, Tony AU - Watson, Oliver AU - Heng, Jerry Y. AU - Papathanasiou, Maria M. ED - VanImpe, J ED - Léonard, G ED - Bhonsale, SS ED - Polanska, M ED - Logist, F TI - Model-based approach to template-induced macromolecule crystallisation T2 - Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35) PB - PSE Press CY - Hamilton SN - 9781777940331 T3 - Systems and Control Transactions, ISSN 2818-4734 ; 4. PY - 2025 SP - 2504 EP - 2509 PG - 6 DO - 10.69997/sct.131246 UR - https://m2.mtmt.hu/api/publication/36259295 ID - 36259295 AB - Biomacromolecules have intricate crystallisation behaviour due to their size and many interactions in solution and can often only crystallise in narrow ranges of experimental conditions. High solute concentrations are needed for crystal nucleation and growth, exceeding those eluted upstream and therefore preventing the adoption of crystallisation in downstream separation steps. By promoting molecular aggregation and nucleation via a lowered energy barrier, heterogeneous surfaces or templates can relax the supersaturation requirements and widen the crystallisation operating space. Though templates are promising candidates for process optimisation, their experimental testing has generally been limited to small-volume experiments, and quantification of their impact on process intensification and quality metrics at higher volumes remains unexplored. To address the knowledge gap, a model-based investigation of template-induced protein crystallisation systems through evaluation of key metrics is presented. Porous silica nano-particles with three chemical functionalisations (hydroxyl, carboxyl and butyl) are added to batch lysozyme crystallisation experiments at 40ml. Crystallisation population balance models are parametrised with an experimentally-validated parameter estimation methodology and further experiments are simulated. The templates appear to lower the estimated interfacial energy compared to the homogeneous case, leading to nucleation rate profiles which are less dependent on supersaturation. For this reason, the templates can crystallize quicker than the homogeneous system, particularly at lower initial concentrations. The simulation results highlight the ability of heteronucleants to alter nucleation rate profiles, and their potential to be used as process optimisation and intensification tools for biomacromolecule purification. LA - English DB - MTMT ER - TY - JOUR AU - Yuan, Xuming AU - Nagy, Zoltan K AU - Benyahia, Brahim TI - A Comprehensive Framework for Model Evaluation and Refinement Using MBDoE Estimability and Structural identifiability: Application to a Crystallization Process JF - CHEMICAL ENGINEERING JOURNAL ADVANCES J2 - CHEM ENG J ADV VL - 23 PY - 2025 SN - 2666-8211 DO - 10.1016/j.ceja.2025.100823 UR - https://m2.mtmt.hu/api/publication/36279709 ID - 36279709 LA - English DB - MTMT ER - TY - JOUR AU - Borsos, Ákos AU - Hámori, Csaba AU - Szilágyi, Emőke AU - Spaits, András AU - Farkas, Ferenc AU - Százdi, László AU - Kátainé Fadgyas, Katalin AU - Volk, Balázs AU - Szilágyi, Botond TI - Derisking Crystallization Process Development and Scale-Up Using a Complementary, “Quick and Dirty” Digital Design JF - ORGANIC PROCESS RESEARCH & DEVELOPMENT J2 - ORG PROCESS RES DEV VL - 28 PY - 2024 IS - 10 SP - 3813 EP - 3826 PG - 14 SN - 1083-6160 DO - 10.1021/acs.oprd.4c00199 UR - https://m2.mtmt.hu/api/publication/35435617 ID - 35435617 N1 - Funding Agency and Grant Number: Egis Pharmaceuticals PLC; Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences; Ministry of Culture and Innovation, from the National Research Development and Innovation Fund [2020-1.1.2-PIACI-KFI-2020-00039]; Ministry of Culture and Innovation, from the National Research Development and Innovation Fund Funding text: The financial support from Egis Pharmaceuticals PLC is gratefully acknowledged. B.S. is thankful for the Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences. This work was prepared in the framework of 2020-1.1.2-PIACI-KFI-2020-00039 project with the support of the Ministry of Culture and Innovation, from the National Research Development and Innovation Fund. AB - Despite the spread of digital (model and AI-based) techniques, the industry-standard pharmaceutical crystallization design and scale-up is still based on experiments’ design (DoE). Many orthogonally designed and usually relatively lightly monitored experiments are performed as a part of it. The final design/scale-up is inherently truncated by experimental and statistical modeling errors and assumptions, compromising the reliability of the calculated design space (DS). This study proposes to apply process modeling in a complementary way: utilize the experiments from the DoE to calibrate an application-driven model, quantify its accuracy, and use it─in parallel with the statistical interpretation of the DoE─to design the process. Both the DoE and model-based DS determination involve workflow-specific assumptions, simplifications, and errors, but the overlap between the independent results may be considered a derisked DS. We demonstrate this workflow on the design of a fed-batch salting-out crystallization for a commercial active pharmaceutical ingredient (API). The model was identified based on product particle size distribution data of a DoE set from a small-scale reactor (0.25 L) and a manufacturing batch (ca. 4000 L). Although reactors with intermediate volumes are also generally applied as a part of scale-up, included in the presented case study, those were not included in the model development and verification. The kinetic equations were taken from our previously developed cooling crystallization model of the same API. After calibration and accuracy evaluation, the critical process parameters were determined using interpretable machine learning via Shapley diagrams, and the DS was mapped and visualized using Monte Carlo sampling-based simulations. The DS was validated for 0.25 L experiments. The model-based DS was somewhat narrower than the DoE-based DS on a small scale. The DS determined for plant-scale crystallization can guide the manufacturing-scale process design and operation. The extrapolation capabilities of the model were stressed by external validation by defining and validating experimentally the DS for a 1 L crystallization. These results indicate that models developed in this application-centric way can enhance the robustness of the processes, and the modeling branch does not add any risk. In the worst-case scenario, if the modeling fails, one still has the results from the traditional design approach. © 2024 The Authors. Published by American Chemical Society. LA - English DB - MTMT ER - TY - JOUR AU - Orosz, Álmos AU - Szilágyi, Botond TI - Maximizing similarity: Using correlation coefficients to calibrate kinetic parameters in population balance models JF - HELIYON J2 - HELIYON VL - 10 PY - 2024 IS - 22 PG - 14 SN - 2405-8440 DO - 10.1016/j.heliyon.2024.e39851 UR - https://m2.mtmt.hu/api/publication/35583409 ID - 35583409 AB - Crystallization plays a crucial role as a separation and purification technique, particularly in the chemical and pharmaceutical industries. By adjusting process parameters, the productivity, product quality, and efficiency of downstream processes can be improved. In complex processes, model-based design becomes invaluable. Population Balance Models (PBMs) have successfully aided the chemical industry in achieving more effective production processes for decades. These models can utilize various input data sources to identify the dominant mechanisms and calibrate model parameters. While inline particle monitoring tools serve as excellent qualitative descriptors, challenges arise from experimentation and data interpretation, hindering their direct application in the kinetic parameter estimation of PBMs. In this study, we present a novel approach that utilizes information from inline particle monitoring tools for the kinetic parameter estimation of PBMs, bypassing the associated obstacles. Our pioneering approach relies on offline product size data and the correlation-based utilization of inline particle monitoring information. The paper compares this novel strategy with two parameter estimation techniques: the classical method using solute concentration and product size data and a somewhat naïve approach, which assumes that the inline particle monitoring data can directly be compared with the simulations. The prediction capabilities are evaluated through two in-silico case studies. The results indicate that the precision and predictive capability of the correlation-based technique are comparable to the classical approach, both for noisy data and for a system undergoing significant agglomeration and deagglomeration. The use of Pearson's correlation coefficient yields the best results in the novel cases. These findings in in-silico datasets provide a foundation and motivation for the practical application of this idea, unleashing the so-far hidden model development potential of such measurements. © 2024 The Author(s) LA - English DB - MTMT ER -