@article{MTMT:37022738,
	title = {Application of partial differential equation (PDE) and integral calculus to rock forming minerals},
	url = {https://m2.mtmt.hu/api/publication/37022738},
	author = {Achuenu, Ifeanyi and Lekmang, C. Isah and Hyeladi, Dibal and Geoffrey, Mica Kumleng},
	journal-iso = {Sigma&Mu},
	journal = {Sigma&Mu: Journal of Mathematics Education, Mathematics, Statistics and Data Science},
	volume = {4},
	unique-id = {37022738},
	issn = {2987-2030},
	year = {2026},
	eissn = {2987-2049},
	pages = {11-23}
}

@article{MTMT:37361629,
	title = {Automation in Pharmaceutical Crystallization},
	url = {https://m2.mtmt.hu/api/publication/37361629},
	author = {Aprile, Giovanni and Sagmeister, Peter and Devos, Cedric and Udepurkar, Aniket and Destro, Francesco and Molajafari, Fateme and Stelzer, Torsten and Ismail, Hamza Youssef and Griffin, Daniel J. and Braatz, Richard D. and Myerson, Allan S.},
	doi = {10.1021/acs.oprd.6c00057},
	journal-iso = {ORG PROCESS RES DEV},
	journal = {ORGANIC PROCESS RESEARCH & DEVELOPMENT},
	volume = {June 25},
	unique-id = {37361629},
	issn = {1083-6160},
	year = {2026},
	eissn = {1520-586X},
	pages = {&},
	orcid-numbers = {Aprile, Giovanni/0000-0001-6549-3107; Sagmeister, Peter/0000-0002-4326-1775; Devos, Cedric/0000-0002-8154-4872; Udepurkar, Aniket/0000-0001-9958-412X; Destro, Francesco/0000-0002-0977-0672; Stelzer, Torsten/0000-0003-3881-0183; Griffin, Daniel J./0000-0002-5051-2069; Braatz, Richard D./0000-0003-4304-3484; Myerson, Allan S./0000-0002-7468-8093}
}

@article{MTMT:36492957,
	title = {Digital design of crystallization processes using statistical machine learning},
	url = {https://m2.mtmt.hu/api/publication/36492957},
	author = {Barhate, Yash and Kang, Yung Shun and Nazemifard, Neda and Renner, C. Benjamin and Yang, Yihui and Papageorgiou, Charles and Nagy, Zoltan K.},
	doi = {10.1016/j.compchemeng.2025.109518},
	journal-iso = {COMPUT CHEM ENG},
	journal = {COMPUTERS & CHEMICAL ENGINEERING},
	volume = {206},
	unique-id = {36492957},
	issn = {0098-1354},
	year = {2026},
	eissn = {1873-4375},
	orcid-numbers = {Barhate, Yash/0009-0007-6848-2244; Kang, Yung Shun/0009-0004-3266-479X; Papageorgiou, Charles/0000-0001-7959-6289}
}

@article{MTMT:37018173,
	title = {Automatic Design of Wet-Milling-Integrated Batch Crystallization Processes by Process Analytical Technology-Based Split-Range Direct Nucleation Control},
	url = {https://m2.mtmt.hu/api/publication/37018173},
	author = {Bereczki-Horváth, Lili Virág and Szilágyi, Botond},
	doi = {10.1021/acs.oprd.5c00402},
	journal-iso = {ORG PROCESS RES DEV},
	journal = {ORGANIC PROCESS RESEARCH & DEVELOPMENT},
	volume = {30},
	unique-id = {37018173},
	issn = {1083-6160},
	abstract = {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.},
	year = {2026},
	eissn = {1520-586X},
	pages = {861-871},
	orcid-numbers = {Szilágyi, Botond/0000-0001-7777-9612}
}

@article{MTMT:37074679,
	title = {Direct Nucleation Control with External Heating Loop: Process System Engineering, Simulation and Experimental Investigation},
	url = {https://m2.mtmt.hu/api/publication/37074679},
	author = {Budimir Sacher, Josip and Bolf, Nenad and Rogue, Manon},
	doi = {10.3390/cryst16040248},
	journal-iso = {CRYSTALS},
	journal = {CRYSTALS},
	volume = {16},
	unique-id = {37074679},
	abstract = {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.},
	year = {2026},
	eissn = {2073-4352},
	pages = {248},
	orcid-numbers = {Budimir Sacher, Josip/0009-0003-7149-9638; Bolf, Nenad/0000-0001-7005-7561}
}

@article{MTMT:37505573,
	title = {Hydrodynamics–kinetics coupling in stirred-tank para-xylene crystallization: Roles of flow pattern and vortex structure},
	url = {https://m2.mtmt.hu/api/publication/37505573},
	author = {Deng, Zhicheng and Zhang, Ruochen and Wang, Jiarui and Dong, Jiayu and Wang, Simin},
	doi = {10.1016/j.cherd.2026.08.054},
	journal-iso = {CHEM ENG RES DES},
	journal = {CHEMICAL ENGINEERING RESEARCH AND DESIGN},
	volume = {234},
	unique-id = {37505573},
	issn = {0263-8762},
	year = {2026},
	eissn = {1744-3563},
	pages = {68-81}
}

@article{MTMT:37311340,
	title = {Modified method for efficient in-situ fines elimination during crystallization of disodium 5′-ribonucleotide using controllable jet feeding and particle sedimentation},
	url = {https://m2.mtmt.hu/api/publication/37311340},
	author = {Liu, Weiqi and Wei, Guixuan and Zhao, Wang and Luo, Wei and Tong, Lu and Jiang, Yanbin},
	doi = {10.1016/j.powtec.2026.122699},
	journal-iso = {POWDER TECHNOL},
	journal = {POWDER TECHNOLOGY},
	volume = {480},
	unique-id = {37311340},
	issn = {0032-5910},
	year = {2026},
	eissn = {1873-328X}
}

@article{MTMT:36952655,
	title = {Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies},
	url = {https://m2.mtmt.hu/api/publication/36952655},
	author = {Ma, Yiming and Yuan, Xuming and Yewale, Ashish and Benyahia, Brahim},
	doi = {10.1016/j.partic.2026.01.014},
	journal-iso = {PARTICUOLOGY},
	journal = {PARTICUOLOGY},
	volume = {110},
	unique-id = {36952655},
	issn = {1674-2001},
	abstract = {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.},
	keywords = {CRYSTALLIZATION; machine learning; RAMAN-SPECTROSCOPY; Process control; Thermodynamic analysis; Process analytical technology; Polymorphic transformation; digital design; Engineering, Chemical; Data-driven; Needle-like crystals; Batch Cooling Crystallization; POPULATION BALANCE-EQUATIONS; estimability analysis; SOLUBILITY PREDICTION MODELS},
	year = {2026},
	eissn = {2210-4291},
	pages = {223-253}
}

@article{MTMT:37528373,
	title = {Population balance modelling case studies for protein crystallisation: Workflows addressing impurities and heterogeneous seeding},
	url = {https://m2.mtmt.hu/api/publication/37528373},
	author = {Wong, Lok Hang and Mitchell, Niall Anthony and Heng, Jerry Yong Yew},
	doi = {10.1016/j.cherd.2026.06.049},
	journal-iso = {CHEM ENG RES DES},
	journal = {CHEMICAL ENGINEERING RESEARCH AND DESIGN},
	volume = {232},
	unique-id = {37528373},
	issn = {0263-8762},
	abstract = {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.},
	keywords = {IMPURITIES; Crystal growth; solubility; NUCLEATION; EGG-WHITE LYSOZYME; PLATFORM; Population balance modelling; Protein crystallisation},
	year = {2026},
	eissn = {1744-3563},
	pages = {544-565}
}

@article{MTMT:35801953,
	title = {Machine learning analysis of rivaroxaban solubility in mixed solvents for application in pharmaceutical crystallization},
	url = {https://m2.mtmt.hu/api/publication/35801953},
	author = {Alqarni, Mohammed and Alqarni, Ali},
	doi = {10.1038/s41598-024-84741-1},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {15},
	unique-id = {35801953},
	year = {2025},
	eissn = {2045-2322}
}

@article{MTMT:35631684,
	title = {Limited Data based kinetic modeling and optimization of propionic acid synthesis over supported Rh/C catalyst},
	url = {https://m2.mtmt.hu/api/publication/35631684},
	author = {Balogh, László and Bódis, Jenő and Szilágyi, Botond and Bárkányi, Ágnes and Egedy, Attila},
	doi = {10.1016/j.ceja.2024.100693},
	journal-iso = {CHEM ENG J ADV},
	journal = {CHEMICAL ENGINEERING JOURNAL ADVANCES},
	volume = {21},
	unique-id = {35631684},
	issn = {2666-8211},
	year = {2025},
	eissn = {2666-8211},
	orcid-numbers = {Bódis, Jenő/0000-0001-7080-124X; Szilágyi, Botond/0000-0001-7777-9612; Bárkányi, Ágnes/0000-0003-0407-9572; Egedy, Attila/0000-0001-7964-1924}
}

@article{MTMT:36601815,
	title = {A Comparative Study of Strategies for Incorporating Uncertainty in Design Space Determination for Pharmaceutical Manufacturing},
	url = {https://m2.mtmt.hu/api/publication/36601815},
	author = {Moshiritabrizi, Iman and McMullen, Jonathan P. and Wyvratt, Brian M. and McAuley, Kimberley B.},
	doi = {10.1021/acs.iecr.5c03037},
	journal-iso = {IND ENG CHEM RES},
	journal = {INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH},
	volume = {64},
	unique-id = {36601815},
	issn = {0888-5885},
	year = {2025},
	eissn = {1520-5045},
	pages = {21658-21668}
}

@article{MTMT:36372919,
	title = {2D population balance modeling and ML-based multi-objective optimization for the crystallization process of resveratrol},
	url = {https://m2.mtmt.hu/api/publication/36372919},
	author = {Orosz, Álmos and Neal, Monika and Rao, Rekha and Roberts, Christine and Szilágyi, Botond and Nagy, Zoltán K.},
	doi = {10.1002/aic.70094},
	journal-iso = {AICHE J},
	journal = {AICHE JOURNAL},
	volume = {71},
	unique-id = {36372919},
	issn = {0001-1541},
	abstract = {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.},
	year = {2025},
	eissn = {1547-5905},
	orcid-numbers = {Orosz, Álmos/0000-0003-0825-3275; Szilágyi, Botond/0000-0001-7777-9612}
}

@article{MTMT:36051983,
	title = {Streamlining crystallization process scale-up using statistical modeling of experiments and complementary CFD flow fields},
	url = {https://m2.mtmt.hu/api/publication/36051983},
	author = {Orosz, Álmos and Sándor, Levente and Firoozirad, Khadijeh and Pusztai, Éva and Nagy-György, Péter and Szilágyi, Botond},
	doi = {10.1016/j.powtec.2025.120934},
	journal-iso = {POWDER TECHNOL},
	journal = {POWDER TECHNOLOGY},
	volume = {458},
	unique-id = {36051983},
	issn = {0032-5910},
	abstract = {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},
	year = {2025},
	eissn = {1873-328X},
	orcid-numbers = {Pusztai, Éva/0000-0002-1997-2630; Szilágyi, Botond/0000-0001-7777-9612}
}

@article{MTMT:36258769,
	title = {Integrated In Vitro / In Silico Uncertainty Quantification Method for Protein Crystallization Models},
	url = {https://m2.mtmt.hu/api/publication/36258769},
	author = {Pessina, Daniele and Calderon De Anda, Jorge and Heffernan, Claire and Heng, Jerry Y. Y. and Papathanasiou, Maria M.},
	doi = {10.1021/acs.iecr.4c04517},
	journal-iso = {IND ENG CHEM RES},
	journal = {INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH},
	volume = {64},
	unique-id = {36258769},
	issn = {0888-5885},
	year = {2025},
	eissn = {1520-5045},
	pages = {12025-12035},
	orcid-numbers = {Pessina, Daniele/0009-0004-3107-6857; Heng, Jerry Y. Y./0000-0003-2659-5500}
}

@{MTMT:36259295,
	title = {Model-based approach to template-induced macromolecule crystallisation},
	url = {https://m2.mtmt.hu/api/publication/36259295},
	author = {Pessina, Daniele and Anda, Jorge Calderon de and Heffernan, Claire and Tian, Tony and Watson, Oliver and Heng, Jerry Y. and Papathanasiou, Maria M.},
	booktitle = {Proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35)},
	doi = {10.69997/sct.131246},
	unique-id = {36259295},
	abstract = {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.},
	year = {2025},
	pages = {2504-2509}
}

@article{MTMT:36279709,
	title = {A Comprehensive Framework for Model Evaluation and Refinement Using MBDoE Estimability and Structural identifiability: Application to a Crystallization Process},
	url = {https://m2.mtmt.hu/api/publication/36279709},
	author = {Yuan, Xuming and Nagy, Zoltan K and Benyahia, Brahim},
	doi = {10.1016/j.ceja.2025.100823},
	journal-iso = {CHEM ENG J ADV},
	journal = {CHEMICAL ENGINEERING JOURNAL ADVANCES},
	volume = {23},
	unique-id = {36279709},
	issn = {2666-8211},
	year = {2025},
	eissn = {2666-8211}
}

@article{MTMT:35435617,
	title = {Derisking Crystallization Process Development and Scale-Up Using a Complementary, “Quick and Dirty” Digital Design},
	url = {https://m2.mtmt.hu/api/publication/35435617},
	author = {Borsos, Ákos and Hámori, Csaba and Szilágyi, Emőke and Spaits, András and Farkas, Ferenc and Százdi, László and Kátainé Fadgyas, Katalin and Volk, Balázs and Szilágyi, Botond},
	doi = {10.1021/acs.oprd.4c00199},
	journal-iso = {ORG PROCESS RES DEV},
	journal = {ORGANIC PROCESS RESEARCH & DEVELOPMENT},
	volume = {28},
	unique-id = {35435617},
	issn = {1083-6160},
	abstract = {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.},
	year = {2024},
	eissn = {1520-586X},
	pages = {3813-3826},
	orcid-numbers = {Borsos, Ákos/0000-0002-6021-4647; Volk, Balázs/0000-0002-2019-1874; Szilágyi, Botond/0000-0001-7777-9612}
}

@article{MTMT:35583409,
	title = {Maximizing similarity: Using correlation coefficients to calibrate kinetic parameters in population balance models},
	url = {https://m2.mtmt.hu/api/publication/35583409},
	author = {Orosz, Álmos and Szilágyi, Botond},
	doi = {10.1016/j.heliyon.2024.e39851},
	journal-iso = {HELIYON},
	journal = {HELIYON},
	volume = {10},
	unique-id = {35583409},
	abstract = {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)},
	year = {2024},
	eissn = {2405-8440},
	orcid-numbers = {Szilágyi, Botond/0000-0001-7777-9612}
}