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Heat Compensation Calorimeter as Process Analytical Tool to Monitor and Control Bioprocess systems Naresh Mohan, and Senthilkumar Sivaprakasam Ind. Eng. Chem. Res., Just Accepted Manuscript • DOI: 10.1021/acs.iecr.7b01367 • Publication Date (Web): 05 Jul 2017 Downloaded from http://pubs.acs.org on July 6, 2017

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Full length article

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Heat Compensation Calorimeter as Process Analytical Tool to Monitor and Control

3

Bioprocess systems

4 5

Naresh Mohan1

6

Senthilkumar Sivaprakasam1,*

7 8

1

9

of Technology Guwahati, Assam, India.

BioPAT Laboratory, Department of Biosciences and Bioengineering, Indian Institute

10 11

*Correspondence:

12

Dr. Senthilkumar Sivaprakasam,

13

Associate Professor,

14

Department of Biosciences and Bioengineering,

15

Indian Institute of Technology Guwahati,

16

Guwahati – 781039, Assam.

17

Tel: +913612582226

18

Fax: +913612582249

19 20

E-mail: [email protected]

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1 ACS Paragon Plus Environment

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1

Abstract

2

Biocalorimeter is a process analytical tool extensively employed to measure metabolic

3

heat in order to track cellular activity. To eliminate the inherent demerits in the

4

conventional biocalorimeters, an advanced bench scale heat compensation fermentation

5

biocalorimeter was designed. Improvement in calorimetric signal sensitivity for

6

bioprocess monitoring was achieved by ensuring isothermal condition in the reactor, by

7

operating 2 independent PID controls in tandem. Significant reduction in heat loss to the

8

environment was accomplished by incorporating a secondary cascade loop to the

9

cryostat controller. Non-biological heat measured under different process conditions

10

was calibrated and accounted as part of the dynamic heat balance. Sensitivity of the

11

system was further improved by incorporating two individual theromostating setups in

12

the reactor housing viz., a pre-heated bubble column and circulating thermal bath fluid

13

in the head plate. Sensitivity and stability of the calorimetric signal were improved to

14

6.73 mW/L and 0.43 mW/L.h respectively. The performance of the developed

15

calorimeter was evaluated by real time monitoring of hyaluronic acid (HA) fermentation

16

by S. zooepidemicus MTCC 3523 and aerobic growth of P.pastoris GSM 115

17

respectively. The calorimetric signals fingerprinted the changes in cellular metabolism

18

and process behavior with viable precision. Experimentally determined oxy-calorific

19

coefficients, for HA fermentation (684.4 kJ/mol) and P.pastoris growth (471.6 kJ/mol)

20

were in good agreement with theoretical predictions, which validated the reliability and

21

reproducibility of the developed application. The developed high-sensitive calorimeter

22

thus offered a wide scope for its application as a process analytical technology (PAT)

23

tool for bioprocess monitoring.

24



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1

Nomenclature

2

Abbreviations:

3

CH Compensation Heater

4

CQA Critical Quality Attribute

5

CPP Critical Process parameter

6

CTAB Cetyltrimethyl-Ammmonium bromide

7

DAQ Data Acquisition

8

DIC Dissolved Oxygen Indicator Control

9

HA Hyaluronic acid

10

HC Heat Compensation

11

MIC Mass flow Indicator Control

12

OD600 Optical Density measured at 600 nm

13

PAT Process Analytical Technology

14

PIC pH Indicator Control

15

PID Proportional, Integrative and Derivative Control

16

PV Process Variable

17

QBD Quality by Design

18

RI Relative Humidity Indicator

19

SDS Sodium Dodecyl Sulphate

20

TIC Temperature Indicator control

21

W Torque Indicator control

22

Variables and Constants:

23

𝐴𝑗 Area of heat exchange between reactor and jacket [0.07402 𝑚2 ]

24

𝐶𝑝,𝑗 Specific heat capacity of silicone oil (Jacket fluid) [1.5 𝑘𝑔 .𝐾 ]

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𝑘𝐽


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𝑘𝐽

1

𝐶𝑝,𝑟 Specific heat capacity of reaction broth [ 𝑘𝑔 .𝐾 ]

2

ℎ𝑟 Reactor side convective heat transfer coefficient

3

ℎ𝑗 Jacket side convective heat transfer coefficient

4

𝐼𝐶 Compensation heater current [A]

5

𝐾𝐺 Thermal conductivity of borosilicate glass vessel, 1.02

6

𝑚𝑤 Mass of Water [3.47 kg]

7

ṁ𝑠 Mass flow rate of silicone oil [0.344 𝑘𝑔/𝑠]

8

Q Input Power deviation variable [W]

9

𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 Baseline heat rate [W/L]

10

𝑞𝐶 Compensation heat rate [23 W]

11

𝑞𝐵 Biological heat rate [W/L]

12

𝑞𝑆 Agitation heat rate [W/L]

13

𝑞𝐴 Aeration heat loss [W/L]

14

𝑞𝐸 Environmental heat loss [W/L]

15

𝑞𝐽 Heat flow from reaction broth to jacket [W/L]

16

𝑞𝑖𝑛𝑝𝑢𝑡 Heat input [W]

17

𝑞𝑖𝑛𝑝𝑢𝑡 ,𝑠 Steady state heat input [W]

18

𝑇𝑅 Response temperature deviation variable [K]

19

𝑇𝑟 Reactor temperature [K]

20

𝑇𝑟𝑠 Steady state reactor temperature [K]

21

𝑇𝑗 Jacket temperature [K]

22

𝑇𝑗 ,𝑖𝑛 Jacket inlet temperature [K]

23

𝑇𝑗 ,𝑜𝑢𝑡 Jacket outlet temperature [K]

𝑊 𝑚 2 .𝐾 𝑊

𝑚 2 .𝐾

𝑊 𝑚 .𝐾



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1

𝑇𝑎 Ambient temperature [K]

2

t Time [s]

3

𝑈𝑗 𝐴𝑗 Overall heat transfer coefficient toward jacket

4

𝑈𝑒 𝐴𝑒 Overall heat transfer coefficient toward environment

5

𝑉𝐶 Compensation heater Voltage [V]

6

x Agitation rate [RPM]

7

y Air flow rate [LPM]

8

𝑌 𝑄 Heat yield due to Oxygen uptake/Oxycalorific coefficient

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𝑊 𝐾 𝑊 𝐾

𝑂2

9

𝑌𝑄 Heat yield due to Biomass formation 𝑋

10

Greek symbol:

11

δ Thickness of the reactor jacket wall [0.007 m]

12

τ Time constant

13 14 15 16 17 18 19 20 21 22 23 24


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1 Introduction

2

The Process Analytical Technology (PAT) implemented by Food and Drug

3

Administration, (FDA) is a mechanism to ensure uniform bio-product quality based on

4

process design. Consequently, the Critical Quality Attributes (CQA) of the product are

5

dependent on control of Critical Process Parameters (CPP). This in turn requires

6

development and integration of various PAT tools for imparting ‘Quality by Design’

7

(QBD) in bioprocess monitoring and control. The cardinal prerequisite of QBD is to

8

understand the complexities involved in the process;1-2 since steady and stringent CPP

9

during upstream processing, drive the quality and uniformity of biological products.

10

Metabolic heat production data can reveal extensive information on the catabolic and

11

anabolic activities of a living cell; with specific reference to dissipation of excess

12

Gibb’s energy, as enthalpy to the environment.3-6 The pattern and rate of metabolic heat

13

production differs significantly with aerobic, anaerobic, fermentation, methanogenic or

14

phototrophic systems of cellular metabolism.7 Therefore, development of a high

15

sensitivity calorimeter would be indispensable for a high precision fingerprinting of

16

metabolic heat changes in bioprocess systems.

17

The metabolic heat signal is an acknowledged control variable in maintaining critical

18

process parameters (CPPs) in bioprocess with defined parameters.8 In addition to

19

metabolic heat measurement, quantitative interpretation of physiological cellular

20

activity encountered in a biological system6,9 is mandatory. Thus the application of

21

‘Quantitative calorimetry’ integrates complimentary process analytics such as

22

impedance spectroscopy, IR spectroscopy and off gas analyzer with the calorimeter.

23

The complex metabolic machinery of the microorganisms is highly susceptible to

24

conditions prevailing in the conventional calorimeters.4,10 While the conventional



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calorimeters facilitate in-situ measurement of metabolic heat production, they are

2

rendered unsuitable for bioprocess monitoring applications due to high cost, low

3

sensitivity and complex reaction vessel geometry.11

4

Fermentation calorimeters, in addition to being operationally viable as typical

5

bioreactors; concurrently measure heat rate generated in the system through highly

6

sensitive temperature sensors and robust temperature control strategy. Fermentation

7

calorimeters also cater most of the essential prerequisites of bioprocess systems, like the

8

continuous supply of O2, pH neutralization, mixing etc. Its sensitivity can be

9

significantly enhanced by introduction of high sensitivity temperature probes, robust

10

Data Acquisition (DAQ) platform, customized process control and reaction vessel with

11

geometry compatible for bioprocess environment.11 These components significantly

12

amplify the advantage of the proposed calorimetric instrumentation over the stand-alone

13

calorimeters available in market.

14

In this present study, heat compensation (HC) strategy11-13 based fermentation

15

calorimeter was designed and investigated for bioprocess monitoring application. The

16

compensation heater installed in the reactor that reduces and regulates the heating

17

output proportional to the biological heat generated by the organism. This in turn

18

nullified the heat transfer resistances across jacket and reactor. Incorporation of

19

secondary cascade loop to the cryostat controller ensured a constant heat flux, which

20

governs the temperature difference across reactor and jacket side under all process

21

conditions.

22

The robust viability of the developed fermentation calorimeter for bioprocess

23

monitoring applications was successfully demonstrated using P.pastoris and

24

S.zooepidemicus cultivations. P.pastoris is an obligate aerobe and is expected to


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generate higher rate of metabolic heat, owing to its respiratory metabolism, Whereas S.

2

zooepidemicus is a facultative anaerobe commonly used fermentative HA production,

3

characterized by its moderately generated metabolic heat in association with HA

4

fermentation process.14 HA is a heteropolysaccharide of β (1-4) UDP glucuronic acid

5

and β (1-3) UDP N-acetyl glucosamine precursors linked with β (1-3) glycosidic

6

linkage and arranged alternatively.15-17 HA finds extensive use in biomedical and

7

cosmetic applications by virtue of its elasticity and moisture retention property.18

8

Assessment of the developed fermentation calorimeter by individually monitoring the

9

exothermic cultures of P. pastoris and S. zooepidemicus would validate its potential in

10

bioprocess monitoring and control applications. The major objectives of this study are

11

(i) quantifying the sensitivity, resolution and stability of measured calorimetric signal;

12

(ii) quantifying non-biological heat rate terms involved in dynamic heat balance; (iii)

13

deployment of HC strategy for depicting metabolic heat changes associated with

14

P.pastoris and HA fermentation process; and (iv) assessment of performance and

15

reliability of the developed calorimeter application based on heat yield coefficients.

16 17

2 Materials and methods

18

2.1 Fermentation calorimeter

19

Instrumentation and process control strategies in the fermentation calorimeter were

20

designed, developed and validated by our research group. The reactor assembly was

21

fabricated by Biojenik Engineering, Chennai, India. It was equipped with a double

22

jacketed glass vessel by Büchi AG, Flawil, Switzerland. The head plate was provisioned

23

with sensors and ports for nutrient supply, air sparging etc. The glass vessel contained

24

an inner jacket of circulating silicone oil that was laterally insulated with an additional



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vacuum jacket as shown in Fig.1. The total volume of reactor vessel was 5 L with a

2

working volume of 3.5 L. Rapid heat transfer in conventional calorimeters, from the

3

reaction broth to jacket fluid significantly impedes reliable temperature measurements.

4

Therefore, to ensure significant conductive resistance for reliable temperature

5

measurement, the thickness of the reactor wall (δ) was increased in the fermentation

6

calorimeter. Further, heat loss through the headspace was minimized through circulation

7

of bath fluid and thermostating with the operating 𝑇𝑟 . The bubble-column provisioned

8

in the air inlet stream pre-saturated the air entering into the reactor (Fig. 1). Presence of

9

moisture in inlet and exhaust air was measured using relative humidity sensors

10

(Rotronic AG, Bassersdorf, Switzerland). Relative humidity measurements were also

11

used to quantify the material (volume loss) and energy (heat loss) due to aeration during

12

operation. Air flow rate was regulated by mass flow controllers (Bronkhorst high-tech,

13

Ruurlo, Netherland) installed in the air inlet supply. The stirrer motor (Simotics S-

14

1FL6, Siemens AG, Berlin & Münich, Germany) was equipped with an on-line torque

15

measurement. Homogenous mixing conditions were ensured by installing a two-stage

16

Rushton turbine impeller. The concentrations of CO2 and O2 in the gas stream leaving

17

the reactor were analyzed by using exhaust gas analyzer (Ultramat 23, Siemens AG,

18

Berlin & Münich, Germany).

19

2.2 Data Acquisition (DAQ)

20

A robust DAQ platform (cRIO 9075, National Instruments, Austin, Texas, United

21

States) was developed for acquiring real-time process variables of reactor temperature

22

(𝑇𝑟 ), jacket fluid temperature 𝑇𝑗 , compensation voltage (𝑉𝐶 ), compensation current

23

( 𝐼𝐶 ), relative humidity (RH), pH, dissolved oxygen (DO), air flow rate, torque,

24

capacitance, exhaust gaseous phase O2 and CO2 concentration at a sampling rate of 5 s.


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1

The Supervisory Control and Data Acquisition (SCADA) program was developed in-

2

house using graphical programming software (LabVIEW, National Instruments, Austin,

3

Texas, United States). The program involved simultaneous data logging, data

4

processing, signal filtering, graphical plots and control functions (Fig. 2). Major factors

5

and instrumentation that controlled the temperature, pH and feeding rate were also

6

customized in this program.

7

2.3 Calorimetric principles

8

High sensitivity semi-standard platinum resistance thermometers (SPRTs) from

9

Isothermal Technology Ltd, Merseyside, England were installed in the reactor, jacket

10

inlet and outlet (Fig. 1). The 0.1 mK resolution of SPRTs was made possible by using a

11

powerful sigma delta analog to digital converter. Thus, the PRT inputs could be

12

measured with a resolution of 28 µΩ equivalent to 0.07 mK. Two independent PID

13

controls, were incorporated into the design of heat compensation calorimeter which

14

regulated overall heat balance across the reactor (Fig. 1).

15

1st PID loop actuates heating output through compensation heater (CH) (Biojenik

16

Engineering, Chennai, India) continuously. It also maintained the isothermal condition

17

by concomitant reduction in its wattage proportional to the biological heat production

18

rate (𝑞𝐵 ). The 2nd PID loop is an integrated feed forward PID controller in the cryostat

19

(Julabo GmbH, Seelbach, Germany) which maintained uniform 𝑇𝑗 ,𝑖𝑛 through the

20

circulation of silicone oil (Julabo bath fluid Thermal H10). Reactor vessel was further

21

insulated from external environment through the vacuum jacket.

22

Heat Compensation calorimetric system suffers a drawback in measuring heat rate due

23

to the fluctuation in ambient temperature (𝑇𝑎 ), which affects the 𝑇𝑗 ,𝑖𝑛



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1

This drawback was significantly reduced in our apparatus by the following

2

modifications:

3

To minimize the effect of ambient temperature on 𝑇𝑗 ,𝑖𝑛 , an additional PID controller was

4

installed for 2nd PID loop of the cryostat control using LabVIEW. Internal feed-forward

5

PID control in the cryostat was used as secondary control, in response to the

6

corrective manipulated variable from the primary PID control of LabVIEW, with the

7

error quantified based on disturbances to 𝑇𝑗 ,𝑖𝑛 (Fig. 2). Table 1 elaborates the

8

advantages of the Instrumentation and advanced control systems of our reactor with

9

respect to the commercial calorimeters. A calibration heater capable of delivering

10

stabilized measurable heat input was installed, for the assessment of heat balance and

11

validation of controller performance.

12

Dynamic energy balance across the calorimeter is governed by the following Eqn. 1 𝑚𝑤 𝐶 𝑝

𝑑𝑇𝑟 = 𝑞𝐶 − 𝑞𝐽 − 𝑞𝐸 + 𝑞𝑆 − 𝑞𝐴 + 𝑞𝐵 𝑑𝑡

13

---Eqn. 1

14

Independent PID control loops were used to maintain 𝑇𝑟 and 𝑇𝑗 ,𝑖𝑛 at their respective set

15

point values, allowing the accumulation term to be practically zero. Real-time

16

measurement of voltage (𝑉𝐶 ) and current (𝐼𝐶 ) permitted the quantification of electrical

17

power wattage ( 𝑉𝐶 𝐼𝐶 ) of CH, 𝑞𝐶 . All the non-biological heat terms were lumped

18

together as baseline heat rate, 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 as given in Eqn. 2 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 = 𝑞𝑆 − 𝑞𝐽 − 𝑞𝐸 − 𝑞𝐴

19

--Eqn. 2

20

Biological heat rate 𝑞𝐵 is the difference between baseline heat rate, 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 and real

21

time measured power output (𝑉𝐶 . 𝐼𝐶 ) as given in Eqn.3. A negative feedback PID control


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1

loop amended the heating output in the compensation heater, proportional to the

2

metabolic heat generated by the system. 𝑞𝐵 = 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 − 𝑞𝐶

3

--- Eqn. 3

4

Heat rate contributions due to non-biological activities were found to be environmental

5

heat loss 𝑞𝐸 , heat flow from reaction broth to jacket 𝑞𝐽 , agitation heat rate 𝑞𝑆 and

6

aeration heat loss 𝑞𝐴 . Precise calibration of non-biological heat rates during various

7

physiological conditions was performed and adequately accounted in 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (Eqn.2).

8

2.4 Controller development

9

Conventional PID controller model was developed by determining process gain, 𝐾𝑝 and

10

time constant, τ of the system. In the calorimeter a known heat input, 𝑞𝑖𝑛𝑝𝑢𝑡 was

11

supplied by the calibration heater incorporated in the system, from which rise in 𝑇𝑟 was

12

recorded as a response, subsequently permitting the determination of process lead, lag,

13

𝐾𝑝 and τ were determined.

14

2.5 Calorimetric baseline experiments

15

Baseline experiments were performed in the developed fermentation calorimeter to

16

assess: 1) the sensitivity of measured heat signal, 2) estimate the non-biological heat

17

fluxes and, 3) study the influence of calorimeter housing on heat signal resolution. In all

18

these baseline experiments, distilled water (Specific heat capacity of water, 𝐶𝑝𝑟 = 4.180

19

𝑘𝐽 𝑘𝑔 .𝐾

) was used as reaction medium (3.5 L) and agitation rate was maintained at 200

20

rpm.

21

2.6 Culture conditions

22

Pichia pastoris GS115 was obtained from Invitrogen USA and preserved at -80°C using

23

30% v/v glycerol. It was pre cultured in 200 mL Yeast Peptone Dextrose medium, and



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incubated for 24 h at 30°C at 200 RPM. Streptococcus zooepidemicus MTCC 3525 was

2

obtained from Institute of Microbial Technology, Chandigarh, India and stored as 30%

3

v/v glycerol stock at -80°C. Preculture was carried out by inoculating the stock culture

4

in 200 mL Todd-Hewitt broth, with overnight incubation at 37°C and agitation rate at

5

180 RPM. Individual precultures of P. pastoris/S. zooepidemicus at 5% v/v of reactor

6

working volume was transferred aseptically into the reactor vessel containing their

7

respective production media.

8

2.7 Calorimeter operation:

9

i.

Cultivation of S. zooepidemicus MTCC 3523 in the production medium:

10

Production medium was constituted with (in g/L): Glucose 30, Yeast extract (YE) 10,

11

MgSO4 1.5, MnSO4 0.5 and KH2PO4 0.5. pH was maintained at 7.0 by the addition of

12

2M NaOH and HCl as neutralizing agents. Reactor temperature, 𝑇𝑟 was maintained at

13

37ºC.

14

ii.

Cultivation of P. pastoris GS115 in the production medium

15

Production medium was constituted with (in g/L): Glycerol 10, CaSO4.2H2O 0.59,

16

K2SO4 9.1, MgSO4.7H2O 7.45, KOH 2.06, NH4Cl 9 for P. pastoris. Ammonium

17

hydroxide 25 % (v/v) was added to the reaction medium prior to the inoculum for

18

adjusting pH to 5.5. This continued to serve as the nitrogen source for yeast culture,

19

with 2M HCl also being used for pH neutralization. Reactor temperature, 𝑇𝑟 was

20

maintained at 30ºC.

21

In-situ sterilization of calorimetric setup was performed at 1 bar pressure and 121ºC for

22

20 min., using distilled water as reaction medium. In both the cultivations, C-source and

23

YE with mineral salts were sterilized separately and transferred aseptically into the

24

calorimeter reaction vessel. The total volume of reactor vessel was 5 L with the working


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1

volume maintained at 3.5 L. Sterile air was supplied at 1vvm, with the agitation rate of

2

200 rpm maintained during fermentation. Baseline heat rate signal, was maintained for a

3

minimum of 3 h duration before inoculation.

4

2.8 Dielectric Spectroscopy

5

A dielectric probe from Aber Instruments, Aberystwyth, United Kingdom, was

6

installed in the reactor vessel. This is a complimentary process analytical tool capable of

7

measuring capacitance (0 – 400 pF/cm) and conductivity (1 – 40 mS/cm). Capacitance

8

measurements are known to provide indirect quantification of viable cell volume at any

9

point of time during the fermentation process.19 Both the values were measured at a

10

frequency of 10 mHZ and 0.5 mHZ for S.zooepidemicus and P.pastoris respectively and

11

their values were logged at 4 s intervals with Futura software, from Aber Instruments,

12

Aberystwyth, United Kingdom.

13

2.9 Analytical Methods

14

Fermentation broth samples were collected at 2 h intervals and stored at 4˚C. Cell

15

growth was quantified by measuring optical density at 600 nm (OD600). Dry cell weight

16

(DCW) estimation was also done simultaneously for the samples and correlated with

17

OD600 values. Glucose was estimated by glucose oxidase – peroxidase method.20

18

Capsular HA was stripped off from the bacterial cell membrane by the addition of 0.1%

19

v/v sodium dodecyl sulphate (SDS), followed by incubation at 37˚C for 10 min. It was

20

then centrifuged at 10,000 rpm for 10 min. Supernatant containing HA was precipitated

21

by the addition of 4-6 volumes of absolute alcohol, and incubated at 4˚C for 1 h. HA

22

pellet was recovered after centrifugation at 10,000 rpm for 10 min. The pellet was

23

washed twice with alcohol and stored in saline at 4˚C for solubilization. Estimation of

24

HA was carried out by Cetyltrimethyl-Ammmonium bromide (CTAB) method.21 All



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Page 16 of 43

1

the offline assays were done in duplicate and the averaged value was presented in the

2

figures.

3

3 Results

4

3.1 Calorimetric signal resolution

5

Calorimetric signal resolution was represented by standard deviation of 200 data points

6

(SD200) when the system was maintained at controlled process condition. The cascade

7

control strategy exhibited improved and consistent stability of measured heat signal

8

from 0.7 to 0.43 mW/L.h, in comparison to the signal received when using only HC

9

strategy. Further improvement in the sensitivity of heat signal from 34.43 ± 0.85 mW/L

10

to 27.69 ± 0.63 mW/L was achieved by the cascade control methodology. Cascade loop

11

associated with the primary heat compensation control reduced the effect of ambient

12

temperature on 𝑇𝑟 (Fig. 3A & 3B) and 𝑇𝑗 ,𝑖𝑛 (Fig. 3C & 3D) significantly.

13

3.2 Controller tuning

14

Control gain, 𝐾𝑃 and time constant, τ value were obtained from transfer function, while

15

proportional, integral and derivative values were derived from open loop tuning of

16

Ziegler-Nicholas method.22 PID values were obtained from the proportional gain value;

17

by applying time constant and dead time to obtain proportional band, integral and

18

derivative time. The tuning values were simulated and linearized by the Jacobian

19

method. P, I & D values were found to be 0.629, 0.0569 and 0.018 respectively. The P,

20

I & D values obtained through open loop tuning were found to corroborate with

21

linearized values. A sensitivity of 35.92 ± 0.72 mW/L was observed for heat

22

compensation control strategy (Fig. 4C & 4D). Application of these values to the real

23

time system control, allowed the results to be finely tuned with the simulated model.

24


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1

3.3 Non-biological heat contributed to calorimetric signal

2

Heat flow, 𝑞𝐽 from reaction broth to the jacket fluid (silicone oil), was estimated by

3

Eqn. 2 and accounted in 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (Eqn. 2). 𝑞𝐽 = 𝑈𝑗 𝐴𝑗 (𝑇𝑟 − 𝑇𝑗 ,𝑜𝑢𝑡 )

4

---Eqn. 4

5

Supplying a known rate of heat input, 𝑞𝑖𝑛𝑝𝑢𝑡 (5 W) and estimating the temperature

6

difference 𝑇𝑟 − 𝑇𝑗 ,𝑜𝑢𝑡 would estimate overall heat transfer coefficient according to

7

Eqn. 4. Difference in the increase of temperature between reactor and jacket was plotted

8

against the heat input yielded a slope 𝑈𝑗 𝐴𝑗 , of 10.21 ± 0.47 𝑊/𝐾 (Fig. S1). Product of

9

this value with 𝑇𝑟 − 𝑇𝑗 ,𝑜𝑢𝑡

provided the rate of heat flow through jacket in the

10

dynamic heat balance.

11

Environmental heat loss, 𝑞𝐸 term in heat balance (Eqn.1) was estimated by providing

12

𝑞𝑖𝑛𝑝𝑢𝑡 (5 W) to the reaction broth, while the temperature control was switched off. 𝑞𝐸

13

was estimated using Eqn. 5, 𝑞𝐸 = 𝑞𝑆 − 𝑞𝐽 + 𝑚𝑤 𝐶𝑝

𝑑𝑇𝑟 𝑑𝑡

14

--- Eqn. 5

15

Environmental heat loss stands for the heat dissipation across non-jacketed part of

16

calorimeter. Therefore heat exchange to environment by aeration should not be allowed

17

(experiment performed without aeration) and hence, 𝑞𝐴 is not included in Eqn. 5.

18

Biological heat rate, 𝑞𝐵 is zero, since it is a baseline experiment.

19

Eqn. 5 describes the energy balance during the determination of environmental heat

20

loss. 𝑞𝐸 was plotted against (𝑇𝑟 − 𝑇𝑎 ) and the influence of 𝑇𝑎 on 𝑞𝐸 was shown in Fig.

21

5A . 𝑈𝑒 𝐴𝑒 was estimated to be 0.853 ± 0.03 W/K (Fig. 5B).



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Page 18 of 43

1

𝑞𝑆 and 𝑞𝐴 were obtained based on the correlation using its operating parameters.

2

Stirring at various agitation rates (RPM), 𝑞𝑆 was estimated (Fig. S2B) from a power rate

3

equation (Eqn. 6) as,

4 𝑞𝑆 = 2 × 10−8 𝑥 −0.312 5

--- Eqn. 6

6

Where, 𝑥 = Agitation rate (RPM)

7

A linear fit (Eqn. 7) was obtained when the rate of heat loss was plotted (Fig. S3B)

8

against their corresponding air flow rates. Flow control error (0.011 LPM) and

9

temperature measurements (< 0.01 K) had negligible effects in the calorimetric signal

10

(> 5 mW/L). 𝑞𝐴 = − 0.878𝑦 − 0.099

11

--- Eqn.7

12

Where, y = Air flow rate (LPM)

13

Heat flow due to operational error was quantified and its influence was tested over

14

different 𝑇𝑎 (Fig. 6A). Non-biological heat rates in 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (Eqn.2), at different

15

operating conditions were successfully deconvoluted (Fig. 6B). Error due to operating

16

parameters and heat flow terms were calculated as standard deviation (SD) values of

17

respective physical values and non-biological heat rates were given in Table 2. The

18

head plate circulation thermostatting at different operating temperatures and

19

prethermostatting air through bubble column significantly improved resolution of the

20

calorimetric signal (Table 3).

21 22


Page 19 of 43

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1

3.4 Calorimetric evaluation of HA fermentation by S. zooepidemicus

2

Growth of S. zooepidemicus in 30 gL-1 of glucose elucidated distinct lag, log and

3

stationary phases in calorimetric signal (Fig. 7A). In the log phase, the biological heat

4

rate 𝑞𝐵 steadily increased and reached a maximum value of 2.4 W/L and the specific

5

growth rate (μ) was determined as 0.48 h-1. A maximum HA titer of 1.2 g/L was

6

observed at the end of 11 h. The oxycalorific coefficient (YQ/O2) was found to be 684.4

7

kJ/mol (Fig. S5A). This value substantiates the fermentative metabolism of S.

8

zooepidemicus and the biomass heat yield coefficient (YQ/X) was estimated to be 10.68

9

kJ/g (Fig. S5B).

10

3.5 Calorimetric evaluation of aerobic respiration by P.pastoris

11

The aerobic respiratory growth of P. pastoris was investigated using the fermentation

12

calorimeter. Process variables such as heat rate, capacitance and oxygen uptake rate

13

exhibited a synchronicity throughout the cultivation (Fig. 8A). Cumulative profiles (Fig.

14

8B) of capacitance and metabolic heat showed good alignment with the biomass profile.

15

Specific growth was determined to be 0.29 h-1. Heat yield due to oxygen consumption

16

and biomass formation was found to be 471.6 kJ/mol. (Fig. S6A) and 10.25 kJ/g (Fig.

17

S6B) respectively.

18

4 Discussion and Conclusions

19

4.1 Baseline experiments

20

The achieved improvement in sensitivity, in the range (27 – 34 mW/L) of heat signal, in

21

the working volume of 3.5 L showed good correlation with the microcalorimeter

22

operational range; and was well above the range for commercial calorimeters

23

customized for bioprocess applications (Table S). The vacuum sealed jacket

24

significantly reduced the influence of ambient temperature (𝑇𝑎 ) fluctuations on inner



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Page 20 of 43

1

jacket surface. However, the non-insulated section of jacket side (jacket fluid entry

2

connection tubing), could be subjected to jacket temperature 𝑇𝑗 perturbations. The

3

introduction of the cascade loop, created an additional advantage of reducing the CH

4

output disturbance due to 𝑇𝑗 ,𝑖𝑛 fluctuations. Subsequently, CH responded in tandem to

5

any change in constant heat flux from the jacket side, in addition to the biological heat

6

production. Jacket inlet temperature, 𝑇𝑗 ,𝑖𝑛 was maintained effectively by the

7

incorporated cascade loop (Fig. 3C & 3D).

8

4.2 PID controller development for reactor temperature 𝑻𝒓

9

Energy contributions such as heat exchanged through cryostat and variable heat input

10

from CH maintained reactor temperature, 𝑇𝑟 by balancing each other. The time constant,

11

𝜏=

12

In a transfer function for the CH based control, a constant heat input, 𝑞𝑖𝑛𝑝𝑢𝑡 to the

13

system served as an input disturbance variable and a rise in reactor temperature, 𝑇𝑟

14

found to be corresponding to response variable respectively. Eqn. 8 describes the heat

15

balance over reaction vessel.

𝑚 𝑤 𝐶𝑃𝑟 𝑈𝑗 𝐴𝑗

was estimated to be 140.83 s for the CH based controller.23

ṁ𝑠 𝐶𝑝𝑗 𝑇𝑗 ,𝑖𝑛 − 𝑇𝑟 − ṁ𝑠 𝐶𝑝𝑗 𝑇𝑟 − 𝑇𝑗 ,𝑜𝑢𝑡 + 𝑞𝑖𝑛𝑝𝑢𝑡 − 𝑈𝑒 𝐴𝑒 𝑇𝑟 − 𝑇𝑎 = 𝑚𝑤 𝐶𝑝𝑟 16

𝑑𝑇𝑟 𝑑𝑡

--- Eqn. 8

17

Development of control based on electrical heater followed the first order linear system.

18

At steady state conditions, Eqn. 8 gets modified to ṁ𝑠 𝐶𝑝𝑗 𝑇𝑗 ,𝑖𝑛 − 𝑇𝑟𝑠 − ṁ𝑠 𝐶𝑝𝑗 𝑇𝑟𝑠 − 𝑇𝑗 ,𝑜𝑢𝑡 + 𝑞𝑖𝑛𝑝𝑢𝑡 ,𝑠 − 𝑈𝑒 𝐴𝑒 𝑇𝑟𝑠 − 𝑇𝑎 = 0

19 20

--- Eqn. 9 Where, 𝑇𝑟𝑠 Steady state reactor temperature [K]


Page 21 of 43

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1

Subtracting Eqn. 9 from Eqn. 8 leads to the formation of 1st order differential equation,

2

which was simplified by incorporating deviation variables as shown in Eqn.10 and Eqn.

3

11 respectively 𝑞𝑖𝑛𝑝𝑢𝑡 − 𝑞𝑖𝑛𝑝𝑢𝑡 ,𝑠 = 𝑄

4

---Eqn. 10 𝑇𝑟 − 𝑇𝑟𝑠 = 𝑇𝑅

5

--- Eqn. 11

6

Where, 𝑇𝑅 Response temperature deviation variable [K]

7

Substituting Eqn. 10 and Eqn. 11 in Eqn. 8. 𝑑𝑇𝑅 𝑄 = 𝜏 + 𝑇𝑅 𝑑𝑡 𝑚𝑤 𝐶𝑝𝑟

8 9

--- Eqn. 12 Laplace transformation of Eqn. 12 yields Eqn. 13,

𝑌 𝑆 =

1 1 1 + 𝑚𝑤 𝐶𝑝𝑟 𝑠 𝑠 + 1 𝜏

10 11

--- Eqn. 13 Representing Eqn. 13 on normal parametric form, 𝑦 𝑡 =

−𝑡 1 1− 𝑒𝜏 𝑚𝑤 𝐶𝑝𝑟

12 13

--- Eqn. 14 From Eqn. 14, Proportional gain, 𝐾𝑝 =

1 𝑚 𝑤 𝐶𝑝𝑟

𝐾

= 0.695 𝐽

𝑠

14

P values were generated in the developed simulated model by slightly modifying 𝐾𝑃 to

15

a range of 0.5 − 1.45 𝐾𝑃 which also corresponded to a proportional band of 0.5 – 1.0

16

(Fig. 4A-4E). Within these proportional values, a defined heat input of 6W was supplied



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Page 22 of 43

1

to the system and controller output was recorded. Integral and derivative values are

2

known to have insignificant effect over the calorimetric signal. The baseline responded

3

to the power input with a short dead time of 2 to 3 min. Stabilization of baseline signal

4

was observed within a short time for proportional values within 0.6 to 0.7 (Fig. 4C &

5

4D) respectively. Offsets were found to be higher beyond this tuning range (Fig 4A &

6

4B). For the proportional band of 1.0 (Fig. 4E), the stability of baseline signal was poor

7

due to the higher permissible error value in the PID loop.

8

4.3 Estimation of heat transfer resistances

9

𝑈𝑗 𝐴𝑗 was determined to be 10.24

𝑊 𝐾

(Fig. S1). Temperature difference between reactor

10

and jacket side corroborated well with the flux developed across them. This value also

11

found to be in good agreement with the theoretical prediction

12

reaction vessel geometry combined with thermal conductivity of glass vessel (Büchi

13

AG, Flawil, Switzerland) 𝐾𝐺 , which in turn indicated the thickness of the vessel δ, and

14

area of heat transfer, 𝐴𝑗 . Computation of

15

suggests that conductive resistance was more significant than convective resistances.

16

Moreover, theoretical estimation of conductive and convective heat transfer resistances

17

of reactor and jacket side were computed based on fluid flow properties as given in Eqn.

18

15.

𝐾𝐺 𝐴 𝑗

𝛿

; calculated using

𝑊

was found to be 10.78 𝐾 . This value

𝛿

1 1 1 = + 𝐾𝐺 𝑈𝑗 ℎ𝑟

𝐾𝐺 𝐴 𝑗

+ 𝛿

1 ℎ𝑗

19

--- Eqn. 15

20

Convective heat transfer resistances, was estimated using empirical correlations24 for

21

reactor (ℎ ) and jacket side (ℎ ) was found to be 5.2 × 10-4 𝑊 𝐾. 𝑚2 (Appendix A) and 𝑟 𝑗

1

1


Page 23 of 43

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1

4.5513 × 10-4 𝑊 𝐾. 𝑚2 respectively (Appendix B). The convective heat transfer

2

resistances in the reactor and the jacket side was significantly less than conductive

3

resistance, 6.87 × 10-3

4

transfer could be neglected in calculating 𝑈𝐽 𝐴𝐽 . Therefore all the above approaches

5

resulted in a concorded 𝑈𝑗 𝐴𝑗 value.

6

4.4 Estimation of non-biological heat

7

Environmental heat transfer coefficient, 𝑈𝑒 𝐴𝑒 was reduced to a value of 0.853 ± 0.03

8

W/K (Fig. 5B) by the lateral vacuum insulation and head plate circulation. This

9

arrangement significantly limited heat loss to the environment, in spite of the stronger

10

driving force generated (𝑇𝑟 − 𝑇𝑎 ). Heat loss rate of 0.15 – 2.5 W/L due to external

11

environment was estimated at various ambient temperatures and subsequently

12

accounted in the 𝑞𝐵𝑎𝑠𝑒𝑙𝑖𝑛𝑒 (Eqn. 2).

13

Continuous real-time measurement of RPM from the stirrer motor facilitated the

14

estimation of heat input due to stirring. The increase in stirring rate was concomitantly

15

compensated by the CH. Influence of agitation in the calorimetric signal was very

16

narrow and exhibited a sharper response (Fig. S2A). Maximum heat rate of 4.5 W/L

17

was observed for an agitation rate of 700 rpm. Thus Eqn. 6 agrees well with the

18

reduction in CH wattage as evident from Fig. S2A.

19

Evaluation of the influence of aeration on the measured heat signal, involved stripping

20

of water from the aqueous reaction medium to effect appropriate humid conditions.

21

Also, heat loss due to evaporation enthalpy is the most singular factor that leads to a

22

deviation of 𝑇𝑟 ; which was less than 0.02ºC of its set point value, under standard

23

operation, involving all heat flow terms as indicated in Eqn. 2. At various mass flow

24

rates, the rate of heat loss due to evaporation was quantified (Fig. S3A), and the

𝛿 𝐾𝐺

through the glass vessel. Consideration of convective heat



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Page 24 of 43

1

calibration plot obtained for various flow rates was described in Eqn. 7. Segregation of

2

non-biological heat, (Fig. 6B) based on the model equations developed, corroborated

3

well with the overall energy balance across the reactor, and thus maintained a stable

4

baseline. Operating error due to physical condition and their corresponding heat

5

contributions were provided in Table. 2

6

4.5 Effect of calorimeter housing on heat signal resolution

7

The resolution of baseline signal increased significantly after thermostating the head

8

plate with the bath fluid to 𝑇𝑟 (Fig. S4A & S4B). The reactor setup was subjected to an

9

ambient temperature fluctuation at a range of 27 ± 6°C. Table 3 shows various

10

configurations of reactor set up and their corresponding improvement in signal

11

resolution. Insulation of reactor vessel with vacuum jacket significantly reduced the

12

fluctuation due to ambient temperature. Calorimetric signal is highly susceptible to the

13

ambient temperature fluctuations without thermostating the head plate (Fig. S4A). A

14

resolution of 15.02 mW/L was achieved with thermostating the head plate (Fig. S4B).

15

Furthermore, the resolution was improved by 2.5 fold by dousing head-plate with

16

circulating bath fluid which simultaneously enhanced the stability of the signal. The

17

resolution observed with set point values for 𝑇𝑟 at 30°C and 35°C were found to be

18

18.69 mW/L and 15.6 mW/L respectively (Table 3). The heat signal resolution

19

estimated in this study were found to be better than those reported for reactor housings

20

used in commercial heat flux calorimeters (Table S).

21

4.6 Application of fermentation calorimeter for monitoring exothermic yeast and

22

bacterial system

23

The suitability of developed fermentation calorimeter was assessed by S. zooepidemicus

24

and P. pastoris cultivation. From the Fig. 7A & 8A, it is evident that calorimetric


Page 25 of 43

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1

signals fingerprint the metabolic activity of P. pastoris and S. zooepidemicus at

2

different phases of the cell growth process. Metabolic activity in both the bioprocess

3

systems exhibited a faster response to the calorimetric signal, in comparison to other

4

process analytical tools (dielectric spectroscopy and exhaust gas analyzer). Linear

5

increase in capacitance profile was in conformity with the calorimetric signal. The

6

developed cascade control was competent in regulating the wattage of CH; where the

7

corresponding change in 𝑞𝐽 and the heat accumulation in the reaction medium were

8

observed to be insignificant. A complete utilization of glucose resulted in the heat rate

9

of 2.4 W/L for HA fermentation by S. zooepidemicus. Biosynthesis of HA is primarily

10

dependant on the cell growth and the cumulative heat was observed to be in good

11

agreement with the off-line biomass concentration data (Fig. 7B). HA is a high-

12

molecular weight polymer and its production by bacterial fermentation involves

13

excessive metabolic stress, which is evident from the resultant calorimetric profile.

14

Cumulative heat profile of yeast (Fig. 8B) was also found to synchronize with offline

15

biomass concentration and oxygen utilization rates. The batch fermentation was

16

completed followed by the exhaustion of growth limiting substrates viz., glucose and

17

glycerol in their respective media. The baseline heat signal was re-established in the

18

calorimetric profile, when the limiting substrate was exhausted; which ensured the

19

robustness and reliability of developed calorimeter application.

20

Heat yield from oxygen consumption, biomass and product formation conveys crucial

21

information for assessing the cell metabolism in an on-going bioprocess. Two

22

distinctive oxy-calorific heat yields observed for P. pastoris (471.6 kJ/mol) and S.

23

zooepidemicus (684.4 kJ/mol) substantiates their facultative anaerobic and aerobic

24

metabolism respectively. Moreover, the corroboration of experimental and theoretical



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Page 26 of 43

1

oxy-calorific heat yields reinforces the successful performance of developed calorimeter

2

for monitoring different kinds of biological systems. This investigation led to the first

3

time report and identification of 10.68 kJ/g as the heat yield coefficient for S.

4

zooepidemicus growth and P. pastoris growth yielded 10.25 kJ/g remained close to the

5

reported value.8 Temperature control over the course of fermentation was robust and the

6

increase in 𝑇𝑟 was less than 0.03K above the set point (Fig. S7), which is a significant

7

contribution of the developed application. Design of specific growth rate estimator

8

based on real-time calorimetric measurements would help in controlling fed-batch

9

production of HA in near future (Fig.S8).

10

Biological processes are typically non-linear/dynamic systems and the development of a

11

reliable in-line sensor is the need of the hour for enhanced monitoring and control

12

applications. The designed and developed fermentation calorimeter application could be

13

readily integrated to an existing bioreactor, thereby enabling robust in-line process

14

monitoring. The achieved calorimetric sensitivity within the designed working volume

15

and scale is a considerable step forward, in development of sensitive instrumentation.

16

The additional cascade loop over the cryostat regulation is a novel attempt to reduce the

17

influence of 𝑇𝑎 over 𝑇𝑗 ,𝑖𝑛 ; which can be tuned further to improve robustness of the

18

controller. The developed fermentation calorimeter was successfully deployed for

19

investigating HA fermentation by S.zooepidemicus and growth in P.pastoris, which

20

substantiated its potential as PAT tool for real-time biomass monitoring. Corroboration

21

of cumulative heat with biomass growth (HA Fermentation) and elucidation of limiting

22

substrate depletion by calorimetric signal (P.pastoris culture), ensures the scope for

23

application of calorimetric signal as a control variable in near future. Design and

24

implementation of advanced temperature control strategy for the present calorimeter


Page 27 of 43

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1

application remain open for further investigation, which would lead to development of

2

ultra-sensitive calorimeter for monitoring weakly exothermic bioprocesses in near

3

future.

4

Acknowledgement

5

Authors gratefully acknowledge the financial support by Department of Biotechnology

6

(DBT),

7

(BT/PR5789/PID/6/680/2012). We feel pleasure to acknowledge Prof. Guhan

8

Jayaraman, IIT Madras and Prof. Kannan Pakshirajan, IIT Guwahati for valuable

9

support and encouragement. Authors would like to personally acknowledge Mr.

10

Jyotiprasad Kakati, Research fellow, Mr. Shubhank Sherekar, Graduate student, Dept.

11

of Biosciences and Bioengineering, Mr. Karteek Yanumula, Research Scholar, Dept. of

12

Electrical and Electronics Engineering, IIT Guwahati and Mr. Anuj Abraham, Research

13

Scholar, Dept. of Instrumentation and Control Engineering, Madras Institute of

14

Technology, Chennai for their valuable support in this work.

15

Supporting Information

16

Appendix, Plots containing Overall heat transfer coefficient, 𝑈𝐽 𝐴𝐽 estimation, Heat rate

17

contribution due to agitation, 𝑞𝑆 and aeration, 𝑞𝐴 , Effect of thermostatting on

18

Calorimetric signal, Oxycalorific coefficients and heat yield due to biomass formation

19

of S.Zooepidemicus and P.Pastoris, Temperature control profile and Tabulated

20

representation of present study with various calorimetric set up.

21

Conflict of interest

22

Authors filed a provisional patent (Intellectual property of India, Patent Application no.:

23

201731007453) for the calorimeter setup and temperature control strategy.

Govt.

of

India

for

successful

accomplishment

of

this

project

24



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Page 28 of 43

5 References

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Paradigm: Role of Process Integration in Process Optimization for Production of

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(2). Streefland, M.; Martens, D. E.; Beuvery, E. C.; Wijffels, R. H. Process

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Analytical Technology (PAT) tools for the cultivation step in biopharmaceutical

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production. Eng. Life Sci. 2013, 13, 212-223.

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(3). von Stockar, U.; Luuk, A. M.; van der Wielen. Thermodynamics in biochemical engineering. J Biotechnol. 1997, 59, 25-37.

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(4). von Stockar, U.; Maskow, T.; Liu, J.; Marison, I. W.; Patiño, R.

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current situation. J Biotechnol. 2006, 121, 517-533.

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(5). von Stockar, U. Biothermodynamics, Chapter19. Biothermodynamics of live

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cells: Energy dissipation and heat generation in cellular cultures. von Stockar,

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U. Ed, EPFL press: Lausanne, 2013; 475 – 534.

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investigation of fed-batch cultures: Bacillus Sphaericus 1593M. Thermochim.

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control enhances bioproduction from toxic feedstocks – Demonstration of

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bioproduction out of methanol. Biotechnol. Bioeng. 2016, 113(10), 2113-2121.

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(9). Sivaprakasam, S.; Schuler, M. M.; Hama, A.; Hughes, K. M.; Marison, I. W.

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Biocalorimetry as a Process analytical technology process analyzer; robust in-

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line monitoring and control of aerobic fed-batch cultures of crabtree-negative

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yeast cells. Journal of Thermal Analysis & Calorimetry. 2011, 104(1), 75-85.

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bioprocess monitoring by small modifications to a standard bench-scale

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measurement of the Heat of Fermentation, Appl. Microbiol. Biotechnol, 1982,

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(14).

Armstrong, D. C.; Cooney, M. J., Johns M. R. Growth and amino acid

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requirements of hyaluronic acid producing Streptococcus Zooepidemicus. Appl.

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Microbiol. Biotechnol., 1997, 47(3), 309-312.

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(15).

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Hyaluronic Acid from Umbilical Cord J. Am. Chem. Soc. 1954, 76(7), 1753–

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Oueslati, N.; Leblanc, P.; Harscoat-Schiavo, C.; Rondags, E.; Meunier,

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in complex environments and under cross-linked form. Carbohyd. Polym., 2014,

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Engineering, CRC press (Taylor & Francis group): Florida, 2009, 284-325. (23).

Coughanowr, D. R.; LeBlanc, S. E. Controller tuning and process

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identification. Process systems analysis and control ,3rd ed; Tata Mcgraw-Hills:

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New York, 2009; 391-410.

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(24).

Knudsen, J. G.; Hottel, H. C.; Sarofim, A. H.; Wankat, P. C.; Knaebel,

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K. S. Heat & Mass transfer, Chapter-05. Perry’s Chemical Engineers’

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Handbook, 7th ed.; Perry, R. H.; Green, D. W.; Maloney, J. O. Eds. McGraw-

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Page 31 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

Figures

2

Fig.1 Schematic representation of Heat Compensation calorimeter

3

DIC Dissolved Oxygen Indicator Control

4

MIC Mass flow Indicator Control

5

PIC pH Indicator Control

6

RI Relative Humidity Indicator

7

TIC Reactor temperature Indicator control

8

W Torque Indicator control

9

Fig. 2 Temperature control principle with additional cascade loop

10

RTD Jacket - Temperature sensor in the jacket inlet

11

RTD Reactor - Temperature sensor in the reactor

12

PID 𝑇𝑟 - Labview based PID 𝑇𝑟 Compensation heater controller

13

PID 𝑇𝑗 ,𝑖𝑛 Primary – Labview based PID 𝑇𝑗 ,𝑖𝑛 Cryostat controller

14

PID 𝑇𝑗 ,𝑖𝑛 Secondary – Internal Feedforward controller in the cryostat

15

Fig. 3 Comparison of Feedforward & Cascade control strategy

16

A. Effect of Feedforward controller on 𝑇𝑅

17

B. Effect of Feedforward & Cascade controller on 𝑇𝑅

18

C. Effect of Feedforward controller on 𝑇𝐽 ,𝑖𝑛

19

D. Effect of Feedforward & Cascade controller on 𝑇𝐽 ,𝑖𝑛

20

Reactor Temperature (Deg. C)

21

Ambient Temperature (Deg. C)

22

Jacket Inlet Temperature (Deg. C)

23

Jacket Outlet Temperature (Deg. C)

24

Fig. 4 Validation of tuned PID values

25

Proportional values: A. 0.5, B. 0.75, C. 0.6, D. 0.7, E. 1.0

26

Power input (W)

27

Measured Heat signal (W)

28

Fig. 5 Deconvolution of environmental heat loss from baseline signal

29


30

Rate of Environmental Heat loss (W/L)

31

Baseline Signal (W/L)

32

Environmental Heat loss (W)



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1

Fig. 6 Segregation of non-biological heat flow from baseline signal

2

A. Variation in physical parameters over time

3

Power input (W)

4

Air mass flow rate (LPM)

5


6

Agitation rate (RPM)

7

Reactor Temperature (Deg. C)

8 9

Page 32 of 43

B. Dynamic profile of non-biological heat rate over time Compensation Power (W)

10

Heat flow rate through jacket (W/L)

11

Rate of Aeration heat loss (W/L)

12

Rate of Environmental heat loss (W/L)

13

Agitation Heat rate (W/L)

14

Baseline signal (W/L)

15

Fig. 7 Comparative profile of Biological heat rate with parallel process analyzers in the

16

monitoring of S.zooepidemicus growth

17

A. Dynamic profile of Power, Capacitance and OUR values in comparison with offline

18

Biomass, Glucose and Hyaluronic acid concentration,

19

Biological heat rate (W/L)

20

Biomass concentration (g/L)

21

Hyaluronic acid concentration (g/L)

22

Capacitance (pF/cm)

23

Glucose concentration (g/L)

24

Oxygen uptake rate (g/L.h)

25

B. Cumulative profile of Power and total O2 utilization in comparison with Biomass and

26

Hyaluronic acid concentration.

27

Cumulative heat (kJ/L)

28


29

Hyaluronic acid concentration (g/L)

30

Moles of Oxygen consumed (Mol./L)

31

Fig. 8 Comparative profile of Biological heat rate with parallel process analyzers in the

32

monitoring of P.pastoris growth


Page 33 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1

A. Dynamic profile of Power, Capacitance and OUR values in comparison with offline

2

Biomass concentration,

3

Biological heat rate (W/L)

4


5

Capacitance (pF/cm)

6

Oxygen uptake rate (g/L.h)

7

B. Cumulative profile of Power and total O2 utilization in comparison with Biomass

8

concentration.

9

Cumulative heat (kJ/L)

10


11

Moles of Oxygen consumed (Mol./L)

12 13 14 15 16 17 18 19 20

21 22 23 24 25 26

Figures

Fig.1 Schematic representation of Heat Compensation calorimeter



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Fig. 2 Temperature control principle with additional cascade loop


Page 35 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


1 2

3 4 5 6 7

Fig. 3 Comparison of Feedforward & Cascade control strategy



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6

Page 36 of 43

Fig. 4Validation of tuned PID values


Page 37 of 43

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1 2 3 4 5 6 7 8 9


Fig. 5 Deconvolution of environmental heat loss from baseline signal



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6

Page 38 of 43

Fig. 6 Segregation of non-biological heat flow from baseline signal


Page 39 of 43

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1 2 3 4


Fig. 7 Comparative profile of Biological heat rate with parallel process analyzers in the monitoring of S.zooepidemicus growth



1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Page 40 of 43

Fig. 8 Comparative profile of Biological heat rate with parallel process analyzers in the monitoring of P.pastoris growth


Page 41 of 43

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1 2 3

Tables Table 1 Improved design of Fermentation Calorimeter Modifications

Advantages

Additional vacuum jacket

Thermal insulation towards lateral surface minimizes effect due ambient temperature.

Incorporation of Cascade loop to the 2nd PID

Reduction in the effect of ambient temperature

circuit of cryostat

fluctuations

Circulation of bath fluid through head plate

Heat loss minimization through head plate

Thickened internal jacket wall (7 mm width)

Prevention of instantaneous heat dissipation from reaction broth to jacket

Bubble column installed prior to sterile inlet

Pre-saturated air minimizes the disturbance due

filter was maintained closer to the reactor

𝑞𝐸

temperature to pre-saturate air. 4 5 6 7

8 9 10 11 12 13 14 15

Table 2 Errors associated in the measurement of non-biological heat flow terms Heat flow terms Physical Operating Error in heat parameter

errora

flow

𝑇𝑅 , 𝑇𝐽 ,𝑖𝑛 , 𝑇𝑗 ,𝑜𝑢𝑡

K

0.1 mK

2 mK

𝑞𝑆

RPM

0.0023 RPM

4.52 mW/L

𝑞𝐴

LPM

0.011 LPM

48.18 mW/L

𝑞𝐸

𝑈𝐸 𝐴𝐸

0.183 W/K

187.84 mW/L

𝑞𝐽

𝑈𝐽 𝐴𝐽

0.47 W/K

11.75 mW/L

𝑞𝐶

𝑉𝐶 . 𝐼𝐶

0.013 W

3.71 mW/L

a – Standard deviation at 200 datapoints at an uniform condition, RPM – Revolutions per min., LPM - Mass flow rate of air



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1 2 3

Page 42 of 43

Table 3 Influence of ambient temperature fluctuations over various pretreatments Uncertainty introduced to

Resolution

4

the system Ambient temperature

15.02 ± 0.86 mW/L

fluctuation at 25°C Ambient temperature fluctuations (Thermal fluid circulation in head plate) At 25°C

06.73 ± 0.4 mW/L

At 30°C

18.69 ± 0.35 mW/L

At 35°C

15.60 ± 1.3 mW/L

Effect of Aeration Without thermostatting

48.18 ± 4.42mW/L

With thermostatting

3.08 ± 0.42 mW/L

5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30


Page 43 of 43

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1


Graphic abstract Monitoring Biological heat generation rate

qE

qC

Heat Compensation principle

qB qS

qC qB

qJ

qA 2


Heat Compensation Calorimeter as a Process ... - ACS Publications

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