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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: This research was supported by Hungarian National Scientific Research Fund (OTKA) grant FK-138475.&lt;/pre&gt;

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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2026.09.11. 12:07 Andódy Katalin (BME admin4)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: 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) .&lt;/pre&gt;
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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2025.08.27. 12:25 Andódy Katalin (BME admin4)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: 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.&lt;/pre&gt;
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&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;36372919&quot; target=&quot;_blank&quot;&gt;2D population balance modeling and ML-based multi-objective optimization for the crystallization process of resveratrol&lt;/a&gt;&lt;/div&gt;    &lt;div&gt;		&lt;span class=&quot;journal-title&quot;&gt;AICHE JOURNAL&lt;/span&gt;

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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2025.12.29. 07:11 Szuper Admin (admin)
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	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: 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&apos;s National Nuclear Security Administration under contract DE-NA0003525. Álmos...&lt;/pre&gt;

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            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.</comment>
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      <abstractText>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.</abstractText>
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&lt;div class=&quot;title&quot;&gt;&lt;a href=&quot;/gui2/?mode=browse&amp;params=publication;35435617&quot; target=&quot;_blank&quot;&gt;Derisking Crystallization Process Development and Scale-Up Using a Complementary, “Quick and Dirty” Digital Design&lt;/a&gt;&lt;/div&gt;    &lt;div&gt;		&lt;span class=&quot;journal-title&quot;&gt;ORGANIC PROCESS RESEARCH &amp; DEVELOPMENT&lt;/span&gt;

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&lt;div class=&quot;lastModified&quot;&gt;Utolsó módosítás: 2026.02.10. 10:39 Szatmári Erika (BME admin4)
&lt;/div&gt;




	&lt;pre class=&quot;comment&quot; style=&quot;margin-top: 0; margin-bottom: 0;&quot;&gt;&lt;u&gt;Megjegyzés&lt;/u&gt;: 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 s...&lt;/pre&gt;
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      <abstractText>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)</abstractText>
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