# Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant

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## Abstract

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## 1. Introduction

## 2. Approach

#### 2.1. Process Description and Control Structure

**Figure 1.**Process flowsheet of a section of a continuous pharmaceutical pilot plant that consists of two crystallizers in series (Cr1–2), a washing and filtration stage (W1), and a buffer tank that can be used for dilution (D1). The section has three automated feedback level control loops (LC1–3), two automated temperature control loops (TC1–2) and an automated feedback concentration control loop (CC1).

#### 2.2. Process Modeling and Parameter Estimation

**Table 1.**Tuning parameters and setpoints of the studied controllers: P, implemented in the pilot plant (proportional only); PI-ALC, implemented in a process simulator (proportional-integral tuned according to averaging level-control criteria); OALC, implemented in process simulator (optimal averaging level control).

Controller | Setpoint | ${\mathit{K}}_{\mathit{c}}$ | ${\mathit{\tau}}_{\mathit{c}}$ | Comments | |
---|---|---|---|---|---|

LC1 | P | $1.05\times {10}^{-2}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $8.3\times {10}^{-4}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | – | |

LC2 | P | $1.15\times {10}^{-2}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $8.3\times {10}^{-4}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | – | |

PI-ALC | $1.24\times {10}^{-2}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $7.5\times {10}^{-4}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | $5.0\times {10}^{3}\phantom{\rule{4pt}{0ex}}\text{s}$ | ||

OALC | $1.24\times {10}^{-2}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $7.5\times {10}^{-6}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | $5.0\times {10}^{5}\phantom{\rule{4pt}{0ex}}\text{s}$ | ${V}_{m}={V}_{SP}\pm 0.20\times {10}^{-2}\phantom{\rule{4.pt}{0ex}}{\text{m}}^{3}$ | |

LC3 | P | $3.00\times {10}^{-3}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $1.7\times {10}^{-3}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | – | |

PI-ALC | $3.08\times {10}^{-3}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $3.3\times {10}^{-4}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | $1.4\times {10}^{4}\phantom{\rule{4pt}{0ex}}\text{s}$ | ||

OALC | $3.08\times {10}^{-3}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}$ | $3.3\times {10}^{-6}\phantom{\rule{4pt}{0ex}}{\text{s}}^{-1}$ | $1.4\times {10}^{6}\phantom{\rule{4pt}{0ex}}\text{s}$ | ${V}_{m}={V}_{SP}\pm 0.10\times {10}^{-2}\phantom{\rule{4.pt}{0ex}}{\text{m}}^{3}$ | |

CC1 | P | $2.62\times {10}^{-1}$ g/g | $2.5\times {10}^{-5}\phantom{\rule{4pt}{0ex}}{\text{m}}^{3}{\text{s}}^{-1}$ | – | ${w}_{A,SP}=0.24\phantom{\rule{4.pt}{0ex}}\text{if}\phantom{\rule{4.pt}{0ex}}t<26.7\phantom{\rule{4.pt}{0ex}}\text{h}$ |

**Figure 2.**Dynamic development of the volume of crystallizers Cr1 and Cr2 (see Figure 1) for a period of 24 h describing experimentally measured data from level sensors (circles and diamonds) and a model-based computation (solid lines). The volume in each crystallizer is a controlled variable within an automated P-only feedback level control loop (LC1 and LC2). A number of plugging events occurred at $t=30.0$ h in the outlet tubing of crystallizer Cr2.

**Figure 3.**Dynamic development of the outlet flow rates of crystallizers Cr1 and Cr2 (see Figure 1) for a period of 24 h describing experimentally measured data obtained from volumetric pumps (circles and diamonds) and a model-based computation (solid lines). The outlet flow rate of each crystallizer is a manipulated variable within an automated P-only feedback level control loop (LC1 and LC2). A number of plugging events occurred at $t=30.0$ h in the outlet tubing of crystallizer Cr2.

**Figure 4.**Dynamic development of the volume (

**A**) and outlet flow rate (

**B**) of buffer tank D1 (see Figure 1) for a period of 24 h describing experimentally measured data (diamonds) obtained from a level sensor and a volumetric pump (P6) and a model-based computation (solid lines). The outlet flow rate of buffer tank D1 is a manipulated variable, and the level is a controlled variable within an automated P-only feedback level control loop (LC3). Note that a setpoint change of a concentration control loop constructed around buffer tank D1 (CC1) at $t=26.7$ h caused the volume to drop and, furthermore, a number of plugging events occurred at $t=30.0$ h in the outlet tubing of crystallizer Cr2 upstream.

Parameter | Estimated value | Initial guess | Bounds |
---|---|---|---|

Flow rate of stream 1 | $5.0\times {10}^{-4}\phantom{\rule{4.pt}{0ex}}\text{kg}\phantom{\rule{4.pt}{0ex}}{\text{s}}^{-1}$ | $4.9\times {10}^{-4}$ | $\left[2.8\times {10}^{-6},8.3\times {10}^{-4}\right]$ |

Mass fraction of A in stream 1 | $7.4\times {10}^{-2}\phantom{\rule{4.pt}{0ex}}\text{kg}/\text{kg}$ | $1.5\times {10}^{-1}$ | $\left[6.0\times {10}^{-2},7.5\times {10}^{-1}\right]$ |

Slurry liquid fraction at outlet of W1 | $2.1\times {10}^{-1}\phantom{\rule{4.pt}{0ex}}\text{kg}/\text{kg}$ | $6.5\times {10}^{-1}$ | $\left[1.0\times {10}^{-1},8.5\times {10}^{-1}\right]$ |

Initial liquid fraction in Cr1 | $9.7\times {10}^{-1}\phantom{\rule{4.pt}{0ex}}{\text{m}}^{3}/{\text{m}}^{3}$ | $8.8\times {10}^{-1}$ | $\left[6.0\times {10}^{-1},9.8\times {10}^{-1}\right]$ |

Initial liquid fraction in Cr2 | $9.6\times {10}^{-1}\phantom{\rule{4.pt}{0ex}}{\text{m}}^{3}/{\text{m}}^{3}$ | $9.6\times {10}^{-1}$ | $\left[6.0\times {10}^{-1},9.8\times {10}^{-1}\right]$ |

**Figure 5.**Dynamic development of the concentration of compound A in buffer tank D1 (

**A**) and the flow rate of solvent added to tank D1 (see Figure 1) (

**B**) for a period of 24 h describing experimentally measured data (diamonds) obtained from an online density meter and a volumetric pump (P5) and a model-based computation (solid lines). The outlet flow rate of buffer tank D1 is a manipulated variable, and the level is a controlled variable within an automated P-only feedback level control loop (LC3). Note that a setpoint change of a concentration control loop constructed around buffer tank D1 (CC1) caused the volume to drop, and furthermore, a number of plugging events occurred at $t=30.0$ h in the outlet tubing of crystallizer Cr2 upstream.

## 3. Results and Discussion

**Figure 6.**Dynamic development of the volume (

**A**) and outlet flow rate of crystallizer Cr2 (

**B**) as predicted by a dynamic model of the process illustrated in Figure 1. The former variable is a controlled variable, and the latter variable is the manipulated variable within an automated level control loop (LC2). The black solid line (triangles) describes the simulated behavior with P-only feedback control, as was done experimentally. The blue line (circles) is the predicted behavior if PI controllers are implemented with tuning tailored for ALC. The red line (diamonds) describes the predicted behavior if OALC is implemented.

**Figure 7.**Dynamic development of the concentration of compound A in the buffer tank D1 (

**A**) and the flow rate of solvent going into the buffer tank (

**B**) as predicted by a dynamic model of the process illustrated in Figure 1. The former variable is a controlled variable, and the latter variable is the manipulated variable within an automated concentration control loop (CC1). The black line (triangles) describes P-feedback control implemented for crystallizer Cr2 and buffer tank D1, as was implemented experimentally. The blue line (circles) is the predicted behavior if PI-ALC feedback controllers are implemented for crystallizer Cr2 and buffer tank D1. The red line (diamonds) describes the predicted behavior if OALC were implemented for crystallizer Cr2 and buffer tank D1. The concentration control loop utilizes P-feedback control in all cases.

**Figure 8.**Dynamic development of the volume (

**A**) and outlet flow rate of buffer tank D1 (

**B**) as predicted by a dynamic model. The former variable is a controlled variable, and the latter variable is the manipulated variable within an automated level control loop (LC3). The black solid line (triangles) describes the simulated behavior for P-only feedback control, as was done experimentally. The blue line (circles) is the predicted behavior for PI-ALC. The red line (diamonds) describes the predicted behavior for OALC.

**Figure 9.**Dynamic development of the outlet flow rate of buffer tank D1 (

**A**) and volume (

**B**) as predicted by a dynamic model with OALC level control in crystallizer Cr2 and buffer tank D1 for various allowable ranges in volume. In the legend, $\Delta V={V}_{max}-{V}_{min}$, and the numbers in the legend are given in cubic meters.

## 4. Conclusions

## Acknowledgments

## Conflicts of Interest

## References

- Schaber, S.D.; Gerogiorgis, D.I.; Ramachandran, R.; Evans, J.M.B.; Barton, P.I.; Trout, B.L. Economic analysis of integrated continuous and batch pharmaceutical manufacturing: A case study. Ind. Eng. Chem. Res.
**2011**, 50, 10083–10092. [Google Scholar] [CrossRef] - Plumb, K. Continuous processing in the pharmaceutical industry: Changing the mind set. Chem. Eng. Res. Des.
**2005**, 83, 730–738. [Google Scholar] [CrossRef] - Roberge, D.M.; Ducry, L.; Bieler, N.; Cretton, P.; Zimmermann, B. Microreactor technology: A revolution for the fine chemical and pharmaceutical industries? Chem. Eng. Technol.
**2005**, 28, 318–323. [Google Scholar] [CrossRef] - Roberge, D.M.; Zimmermann, B.; Rainone, F.; Gottsponer, M.; Eyholzer, M.; Kockmann, N. Microreactor technology and continuous processes in the fine chemical and pharmaceutical industry: Is the revolution underway? Org. Process Res. Dev.
**2008**, 12, 905–910. [Google Scholar] [CrossRef] - Jimenez-Gonzalez, C.; Poechlauer, P.; Broxterman, Q.B.; Yang, B.S.; Am Ende, D.; Baird, J.; Bertsch, C.; Hannah, R.E.; Dell’Orco, P.; Noorrnan, H.; et al. Key green engineering research areas for sustainable manufacturing: A perspective from pharmaceutical and fine chemicals manufacturers. Org. Process. Res. Dev.
**2011**, 15, 900–911. [Google Scholar] [CrossRef] - LaPorte, T.L.; Wang, C. Continuous processes for the production of pharmaceutical intermediates and active pharmaceutical ingredients. Curr. Opin. Drug Discovery Dev.
**2007**, 10, 738–745. [Google Scholar] - Kockmann, N.; Gottsponer, M.; Zimmermann, B.; Roberge, D.M. Enabling continuous-flow chemistry in microstructured devices for pharmaceutical and fine-chemical production. Chem. Eur. J.
**2008**, 14, 7470–7477. [Google Scholar] [CrossRef] [PubMed] - Hartman, R.L.; McMullen, J.P.; Jensen, K.F. Deciding whether to go with the flow: Evaluating the merits of flow reactors for synthesis. Angew. Chem. Int. Ed.
**2011**, 50, 7502–7519. [Google Scholar] [CrossRef] [PubMed] - Wegner, J.; Ceylan, S.; Kirschning, A. Ten key issues in modern flow chemistry. Chem. Commun.
**2011**, 47, 4583–4592. [Google Scholar] [CrossRef] [PubMed] - Wegner, J.; Ceylan, S.; Kirschning, A. Flow chemistry— A key enabling technology for (multistep) organic synthesis. Adv. Synth. Catal.
**2012**, 354, 17–57. [Google Scholar] [CrossRef] - Pollet, P.; Cope, E.D.; Kassner, M.K.; Charney, R.; Terett, S.H.; Richman, K.W.; Dubay, W.; Stringer, J.; Eckertt, C.A.; Liotta, C.L. Production of (S)-1-benzyl-3-diazo-2-oxopropylcarbamic acid tert-butyl ester, a diazoketone pharmaceutical intermediate, employing a small scale continuous reactor. Ind. Eng. Chem. Res.
**2009**, 48, 7032–7036. [Google Scholar] [CrossRef] - Christensen, K.M.; Pedersen, M.J.; Dam-Johansen, K.; Holm, T.L.; Skovby, T.; Kiil, S. Design and operation of a filter reactor for continuous production of a selected pharmaceutical intermediate. Chem. Eng. Sci.
**2012**, 26, 111–117. [Google Scholar] [CrossRef] - Chen, J.; Sarma, B.; Evans, J.M.B.; Myerson, A.S. Pharmaceutical crystallization. Cryst. Growth Des.
**2011**, 11, 887–895. [Google Scholar] [CrossRef] - Griffin, D.W.; Mellichamp, D.A.; Doherty, M.F. Reducing the mean size of API crystals by continuous manufacturing with product classification and recycle. Chem. Eng. Sci.
**2010**, 65, 5770–5780. [Google Scholar] [CrossRef] - Wong, S.Y.; Tatusko, A.P.; Trout, B.L.; Myerson, A.S. Development of continuous crystallization processes using a single-stage mixed-suspension, mixed-product removal crystallizer with recycle. Cryst. Growth Des.
**2012**, 12, 5701–5707. [Google Scholar] [CrossRef] - Alvarez, A.J.; Myerson, A.S. Continuous plug flow crystallization of pharmaceutical compounds. Cryst. Growth Des.
**2010**, 10, 2219–2228. [Google Scholar] [CrossRef] - Alvarez, A.J.; Singh, A.; Myerson, A.S. Crystallization of Cyclosporine in a multistage continuous MSMPR crystallizer. Cryst. Growth Des.
**2011**, 11, 4392–4400. [Google Scholar] [CrossRef] - Lawton, S.; Steele, G.; Shering, P.; Zhao, L.H.; Laird, I.; Ni, X.W. Continuous crystallization of pharmaceuticals using a continuous oscillatory baffled crystallizer. Org. Process Res. Dev.
**2009**, 13, 1357–1363. [Google Scholar] [CrossRef] - Eder, R.J.P.; Schmitt, E.K.; Grill, J.; Radl, S.; Gruber-Woelfler, H.; Khinast, J.G. Seed loading effects on the mean crystal size of acetylsalicylic acid in a continuous-flow crystallization device. Cryst. Res. Technol.
**2011**, 46, 227–237. [Google Scholar] [CrossRef] - Eder, R.J.P.; Schrank, S.; Besenhard, M.O.; Roblegg, E.; Gruber-Woelfler, H.; Khinast, J.G. Continuous sonocrystallization of acetylsalicylic acid (ASA): Control of crystal size. Cryst. Growth Des.
**2012**, 12, 4733–4738. [Google Scholar] [CrossRef] - Quon, J.; Zhang, H.; Alvarez, A.J.; Evans, J.M.B.; Myerson, A.S.; Trout, B.L. Continuous crystallization of aliskiren hemifumarate. Org. Process Res. Dev.
**2012**, 12, 3036–3044. [Google Scholar] [CrossRef] - Zhang, H.; Quon, J.; Alvarez, A.J.; Evans, J.M.B.; Myerson, A.S.; Trout, B.L. Development of continuous anti-solvent/cooling crystallization process using cascaded mixed suspension, mixed product removal crystallizers. Org. Process Res. Dev.
**2012**, 16, 915–924. [Google Scholar] [CrossRef] - Mortier, S.T.F.C.; De Beer, T.; Gernaey, K.V.; Vercruysse, J.; Fonteyne, M.; Remon, J.P.; Vervaet, C.; Nopens, I. Mechanistic modelling of the drying behaviour of single pharmaceutical granules. Eur. J. Pharm. Biopharm.
**2012**, 80, 682–689. [Google Scholar] [CrossRef] [PubMed] - Gonnissen, Y.; Remon, J.P.; Vervaet, C. Development of directly compressible powders via co-spray drying. Eur. J. Pharm. Biopharm.
**2007**, 67, 220–226. [Google Scholar] [CrossRef] [PubMed] - Gonnissen, Y.; Goncalves, S.I.V.; De Geest, B.G.; Remon, J.P.; Vervaet, C. Process design applied to optimise a directly compressible powder produced via a continuous manufacturing process. Eur. J. Pharm. Biopharm.
**2008**, 68, 760–770. [Google Scholar] [CrossRef] [PubMed] - Wang, M.; Rutledge, G.C.; Myerson, A.S.; Trout, B.L. Production and characterization of carbamazepine nanocrystals by electrospraying for continuous pharmaceutical manufacturing. J. Pharm. Sci.
**2012**, 101, 1178–1188. [Google Scholar] [CrossRef] [PubMed] - Brettmann, B.; Bell, E.; Myerson, A.; Trout, B. Solid-state NMR characterization of high-loading solid solutions of API and excipients formed by electrospinning. J. Pharm. Sci.
**2012**, 101, 1538–1545. [Google Scholar] [CrossRef] [PubMed] - Brettmann, B.K.; Cheng, K.; Myerson, A.S.; Trout, B.L. Electrospun formulations containing crystalline active pharmaceutical ingredients. Pharm. Res.
**2013**, 30, 238–246. [Google Scholar] [CrossRef] [PubMed] - Dubey, A.; Vanarase, A.U.; Muzzio, F.J. Impact of process parameters on critical performance attributes of a continuous blender A DEM-based study. AIChE J.
**2012**, 58, 3676–3684. [Google Scholar] [CrossRef] - Dubey, A.; Sarkar, A.; Ierapetritou, M.; Wassgren, C.R.; Muzzio, F.J. Computational approaches for studying the granular dynamics of continuous blending processes, 1-DEM based methods. Macromol. Mater. Eng.
**2011**, 296, 290–307. [Google Scholar] [CrossRef] - Portillo, P.M.; Ierapetritou, M.G.; Muzzio, F.J. Effects of rotation rate, mixing angle, and cohesion in two continuous powder mixers–A statistical approach. Powder Technol.
**2009**, 194, 217–227. [Google Scholar] [CrossRef] - Hamdan, I.M.; Reklaitis, G.V.; Venkatasubramanian, V. Exceptional events management applied to roller compaction of pharmaceutical powders. J. Pharm. Innov.
**2010**, 5, 147–160. [Google Scholar] [CrossRef] - Singh, R.; Ierapetritou, M.; Ramachandran, R. An engineering study on the enhanced control and operation of continuous manufacturing of pharmaceutical tablets via roller compaction. Int. J. Pharm.
**2012**, 438, 307–326. [Google Scholar] [CrossRef] [PubMed] - Wiles, C.; Watts, P. Continuous flow reactors: A perspective. Green Chem.
**2012**, 14, 38–54. [Google Scholar] [CrossRef] - Poechlauer, P.; Manley, J.; Broxterman, R.; Gregertsen, B.; Ridemark, M. Continuous processing in the manufacture of active pharmaceutical ingredients and finished dosage forms: An industry perspective. Org. Process Res. Dev.
**2012**, 16, 1586–1590. [Google Scholar] [CrossRef] - Singh, R.; Gernaey, K.V.; Gani, R. Model-based computer-aided framework for design of process monitoring and analysis systems. Comput. Chem. Eng.
**2009**, 33, 22–42. [Google Scholar] [CrossRef] - Gernaey, K.V.; Cervera-Padrell, A.E.; Woodley, J.M. A perspective on PSE in pharmaceutical process development and innovation. Comput. Chem. Eng.
**2012**, 42, 15–29. [Google Scholar] [CrossRef] - Cervera-Padrell, A.E.; Skovby, T.; Kiil, S.; Gani, R.; Gernaey, K.V. Active pharmaceutical ingredient (API) production involving continuous processes—A process systems engineering (PSE)-assisted design framework. Eur. J. Pharm. Biopharm.
**2012**, 82, 437–456. [Google Scholar] [CrossRef] [PubMed] - Gernaey, K.V.; Gani, R. A model-based systems approach to pharmaceutical product-process design and analysis. Chem. Eng. Sci.
**2010**, 65, 5757–5769. [Google Scholar] [CrossRef] - Gernaey, K.V.; Cervera-Padrell, A.E.; Woodley, J.M. Development of continuous pharmaceutical production processes supported by process systems engineering methods and tools. Future Med. Chem.
**2012**, 4, 1371–1374. [Google Scholar] [CrossRef] [PubMed] - Boukouvala, F.; Niotis, V.; Ramachandran, R.; Muzzio, F.J.; Ierapetritou, M.G. An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process. Comput. Chem. Eng.
**2012**, 42, 30–47. [Google Scholar] [CrossRef] - Benyahia, B.; Lakerveld, R.; Barton, P.I. A plant-wide dynamic model of a continuous pharmaceutical process. Ind. Eng. Chem. Res.
**2012**, 51, 15393–15412. [Google Scholar] [CrossRef] - Lakerveld, R.; Benyahia, B.; Braatz, R.D.; Barton, P.I. Model-based design of a plant-wide control strategy for a continuous pharmaceutical plant. AIChE J.
**2013**, 59, 3671–3685. [Google Scholar] [CrossRef] [Green Version] - McDonald, K.A.; McAvoy, T.J.; Tits, A. Optimal averaging level control. AIChE J.
**1986**, 32, 75–86. [Google Scholar] [CrossRef] - Mascia, S.; Heider, P.L.; Zhang, H.; Lakerveld, R.; Benyahia, B.; Barton, P.I.; Braatz, R.D.; Cooney, C.L.; Evans, J.M.B.; Jamison, T.F.; et al. End-to-end continuous manufacturing of pharmaceuticals: Integrated synthesis, purification, and final dosage formation. Angew. Chem. Int. Ed.
**2013**, 52, 12359–12363. [Google Scholar] [CrossRef] [PubMed] - Seborg, D.E.; Edgar, T.F.; Mellichamp, D.A.; Doyle, F.J., III. Process Dynamics and Control, 3rd ed.; John Wiley & Sons: Hoboken, NJ, USA, 2011; pp. 107–108. [Google Scholar]
- St. Clair, D.W. Controller Tuning and Control Loop Performance: “PID without the Math”, 2nd ed.; Straight-line Control Co. Inc.: Newark, NJ, USA, 1993. [Google Scholar]
- Campo, P.J.; Morari, M. Model predictive optimal averaging level control. AIChE J.
**1989**, 35, 579–591. [Google Scholar] [CrossRef]

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## Share and Cite

**MDPI and ACS Style**

Lakerveld, R.; Benyahia, B.; Heider, P.L.; Zhang, H.; Braatz, R.D.; Barton, P.I.
Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant. *Processes* **2013**, *1*, 330-348.
https://doi.org/10.3390/pr1030330

**AMA Style**

Lakerveld R, Benyahia B, Heider PL, Zhang H, Braatz RD, Barton PI.
Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant. *Processes*. 2013; 1(3):330-348.
https://doi.org/10.3390/pr1030330

**Chicago/Turabian Style**

Lakerveld, Richard, Brahim Benyahia, Patrick L. Heider, Haitao Zhang, Richard D. Braatz, and Paul I. Barton.
2013. "Averaging Level Control to Reduce Off-Spec Material in a Continuous Pharmaceutical Pilot Plant" *Processes* 1, no. 3: 330-348.
https://doi.org/10.3390/pr1030330