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퍼지 추론 기반 Servo HPLC Prep Column 제어

권오영1, 박형석2, 오석근2, 홍대희1,#orcid

Automation Control System for Servo Prep Column of High-Performance Liquid Chromatography Based on Fuzzy Inference Control

Oyoung Kwon1, Hyung Suk Park2, Seok Geun Oh2, Daehie Hong1,#orcid
Journal of the Korean Society for Precision Engineering 2019;36(12):1135-1140.
Published online: December 1, 2019

1 고려대학교 기계공학부

2 ㈜영진바이오크롬

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#E-mail: dhhong@korea.ac.kr, TEL: +82-2-3290-3369, FAX: +82-2-3290-3864
• Received: February 22, 2018   • Revised: August 26, 2019   • Accepted: October 23, 2019

Copyright © The Korean Society for Precision Engineering

This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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  • This paper describes a control system for the servo prep column of High-Performance Liquid Chromatography (HPLC) based on fuzzy inference control. The key technology in pharmaceutical and biotechnology industries is refining performance and efforts to reduce costs by purifying target compounds with high purity at high yield while maintaining target compounds, is the major focus of new product development. Among the many refinement techniques, the most popular chromatographic methods require a column that can charge the resin with excellent performance and reproducibility. However, the present HPLC prep column has a hydraulic for control moving stopper and compressed chemical compound. It always causes irregular performances of the column. This paper presents automation control with a servo motor that prevents slurry issues and improves efficiency of the prep column reproducibility and provides easy automation. As an automation method, cortex-m4 as an embedded processor and operating system with LabVIEW, are used to control the HPLC system. To generate the heuristic data for the fuzzy inference control, experiments are conducted to identify correlation between data such as pressure sensor and motor speed. The result will improve performance of the servo prep column of HPLC for automation control based on fuzzy inference control.
  • KEYWORDS: Automation control system, Fuzzy inference control, High performance liquid chromatography
  • KEYWORDS: 자동화 제어 시스템, 퍼지 컨트롤, 고성능 액체 크로마토그래피

Rulen

Number of rules

MFn

Number of Membership functions

LVn

Number of Linguistic variables

P.K

Packing Variable
오늘날 생명공학기술의 발전으로 의약품 시장의 중심이 합성 의약품에서 바이오 의약품으로 변해가는 추세이다.1 바이오 의약품은 일반적으로 합성 의약품에 비해 크기가 크고 복잡한 구조를 가지고 있으며, 생물체를 이용하여 복잡한 제조공정을 거쳐야 하므로 변화에 민감하고 특히 오염 및 불순물 관리에 유의해야 하는 단점이 있으나, 고유의 독성이 낮고 작용기전이 명확하여 난치성 및 만성질환에 뛰어난 효과를 보여준다. 바이오 의약품의 주요제품으로는 단백질 의약품, 백신, 바이오시밀러, 천연물 의약품 등이 있으며 이중에서도 바이오시밀러 시장규모가 급속한 시장 성장이 예상되고있다. 바이오시밀러란 바이오 의약품의 복제약을 의미하는 개념으로 오리지널 바이오 의약품과 동등한 품질을 지닌 의약품을 지칭하는 의약품이다. 바이오시밀러의 급속한 성장 배경은 오리지널 바이오 의약품의 특허 만료가 2012년부터 본격화 됨에 따라 바이오 의약품에 비해 저렴한 개발 및 제조비용이 소요되는 바이오시밀러 연구 개발이 본격화 될 것으로 예상되기 때문이다.2,3
바이오시밀러 산업에서 가장 큰 핵심 기술은 정제 기술로 목표 화합물을 고 순도로 분리 정제 하여 높은 수율로 정제하는 것이 목표이다. 이러한 정제 기술에 가장 많이 사용하는 방법은 크로마토그래피를 사용한다. 크로마토그래피 방법에서 가장 중요한 부분 중에 하나가 컬럼이다. 컬럼에서의 재현성이 떨어지면 제품 개발에 성공하더라도 양산 과정에서 품질의 일관성이 떨어져 생산 현장에서의 문제를 야기하기 때문이다. 이러한 이유로 바이오시밀러 산업에서 품질에 대한 일관성을 확보할 수 있는 단백질 정제용 컬럼 개발 및 자동화가 아주 중요한 점으로 여겨지고 있다. 크로마토그래피 방법은 다양하게 변형되어 여러 종류의 크로마토그래피를 낳았으며 본 연구에서 사용된 크로마토그래피 방법은 고성능 액체 크로마토그래피(High Performance Liquid Chromatography, HPLC))이다.
고성능 액체 크로마토그래피는 액체를 이동상으로 하는 컬럼크로마토그래피법으로서, 고성능 충진제, 고압 펌프, 고감도 검출기를 갖추고 있어서 미량의 시료를 신속하고 간편하게, 그리고 재현성 있는 분석이 가능하다.4
기존의 HPLC 프렙컬럼의 공정 방법은 유압을 사용한 수동충전 방법으로 무빙스토퍼를 유압실린더의 힘으로 구동시켜 컬럼 안에 있는 화학물을 압축하는 방식이며 이 유압실린더의 경우 에어 구동 방식의 오일 펌프로 작동시킨다.
Fig. 1은 기존의 유압식의 HPLC 프렙컬럼 장치이다. 기존의 HPLC 프렙컬럼 공정 방법은 사용자가 유압실린더 밸브를 이용하여 무빙스토퍼를 조절하고 기기에 부착 되어있는 압력 센서값에 의지하여 사용자는 필요한 압력 값에 맞게 무빙스토퍼의 위치를 조절한다. 이러한 방법은 사용하는 사용자마다 매번 다른 공정 결과를 야기시키는 원인이 되고 매번 사용자의 수동 충전이 필요하여 재현성이 많이 떨어지는 것이 문제점이 되고 있다.
Fig. 1

Hydraulic HPLC prep column (Young Jin Biochrom)

KSPE_2019_v36n12_1135_f001.jpg
이러한 비정형적인 환경에 공정의 일관성을 높이기위해서 무빙스토퍼 위치를 조절하기 위한 기존의 유압 방식을 버리고 전기적 제어가 가능한 AC 서보 모터를 적용 하여 중요한 충전 변수인 압력을 프로그램적으로 제어하여 실시간 정밀 제어가 가능한 서보 HPLC 프렙컬럼을 개발하는 것이 매우 중요하다.
본 논문에서는 기존의 일관성과 재현성이 떨어지는 정제 방법을 개선하고자 퍼지 추론 기반의 서보 HPLC 프렙컬럼 자동화 기술 개발을 제안하고자 한다. 퍼지 추론을 이용하여 실시간으로 측정하는 압력 값과 무빙스토퍼를 구동하기위한 AC 서보모터의 속도 값을 조절하여 무빙스토퍼의 위치를 파악하고 위치에 따른 정제 방식을 적용하여 사용자의 편의성을 고려한 자동화 기술을 제안한다.
2.1 사용자 의도 파악
기존의 HPLC Prep System의 경우 압력을 조절하는 방법은 숙련된 사용자가 압력 게이지를 보면서 유압 밸브를 컨트롤 하는 방법만이 가능하였다. 모든 공정이 숙련된 사용자의 감으로 공정이 진행되었다. 이러한 기존의 방법은 사용자가 느끼는 무빙스토버의 속도의 기준이 다르고 컨트롤 하는 압력의 게이지 상태가 매번 다르기 때문에 공정의 일관성이 많이 떨어지는 것이 가장 문제였다. 이러한 점을 보완하기 위해서 맘다니 퍼지 추론(Mamdani Fuzzy Interface)을 적용하였다. 이 방법은 사용자의 접근성을 수치화 하기 위한 가장 좋은 방법이다.5,6 본 연구에서의 사람의 기준을 수치화 하여 사용자의 의도를 퍼지 추론을 이용하여 파악 하였다.
2.2 퍼지 추론 적용
기동 실험을 통하여 압력 센서와 서보 모터의 RPM의 상관관계를 나타낼 수 있는 값들을 측정하여 퍼지 집합(Fuzzy Set)을 만들었다. Figs. 2, 3과 가이 멤버십함수를 정해주었다. 멤버십 함수 함수에 해당하는 언어적 변수(Linguistic Variable, LV)에 대한 정의는 Table 1과 같이 정의 하였다. 멤버십 함수에 관한 룰은 식(1)에 의해 Table 2와 같이 정의하였다. 본 퍼지 추론에서는 2개의 입력 값을 이용하여 하나의 출력 값을 추론했다. 즉 2개의 입력 값, 압력과 모터 속도를 이용하여 무빙스토퍼의 위치를 추론하기위해 퍼지 추론을 적용한 것이다. HPLC 프렙컬럼의 기계적 특징을 고려해 Table 2와 같은 룰을 적용하여 압력이 매우 낮을 시 모터의 속도를 빠르게 그리고 압력이 매우 높을시는 모터의 속도는 매우 느리게 하여 무빙스토퍼의 위치를 추론하였다. 언어적 변수에서 지정한 OT는 무빙스토퍼가 컬럼 안으로 들어가기 전에 위치를 지정한 변수이고 컬럼 안으로 들어왔을 때는 T 즉 Top이라 지정하였다. IS는 무빙스토퍼가 컬럼 안에서 들어가고있는 가장 낮은 부분을 표현한 변수이다. Table 2에서 기계적인 특성상 값이 나올수 없는 부분은 None으로 처리하여 값을 지정하지 않고 처리하였다.
(1)
Rulen=LVnMFn
Fig. 2

Membership function of pressure

KSPE_2019_v36n12_1135_f002.jpg
Fig. 3

Membership function of motor_rpm

KSPE_2019_v36n12_1135_f003.jpg
Table 1

Linguistic variable

Table 1
PVL Pressure very low
PL Pressure low
PM Pressure middle
PH Pressure high
RVL Rpm very low
RL Rpm low
RM Rpm middle
RF Rpm fast
OT Over top
T Top
M Middle
IS Inner side
Table 2

Fuzzy rule

Table 2
AND RVL RL RM RF
PVL None None T OT
PL None M T OT
PM IS M T None
PH IS M None None
Fig. 4와 같이 무빙스토퍼의 위치를 결과 값으로 지정하여 실시간 압력 값과 Table 3의 충전 변수에 따른 모터 값을 측정하여 멤버십 함수를 정의 하였고 비퍼지화(Defuzzification)의 방법으로는 폐루프 제어(Closed-Loop Control)에 적용이 가능한 범위 중심 방법을 이용하여 결과값을 구하였다.
Fig. 4

Membership function of moving stopper position

KSPE_2019_v36n12_1135_f004.jpg
Table 3

HPLC charge variable

Table 3
No. P.V Data Discretion
1 Column inner 200 Input
2 Column height 367 Input
3 Packing density 0.52 Input
4 Filler 5,992 Output
5 Slurry 23.047 Output
6 Slurry height 734 Output
7 Column pressure 2.5 Input
8 Pump flow 1.884 Output
9 Stopper distance 611 Decision
10 Distance
3.1 STM32F407 Developing Board
Fig. 5는 실험에 사용한 개발 보드의 구성을 보여준다. 개발보드는 상단 파트와 하단 파트로 분리되어 만들어졌다. 상단 보드는 주 MCU (Cortex-M4)와 PC와의 통신을 위한 Usart to USB 회로를 추가하여 수시로 처리되는 데이터를 확인할 수 있게 개발되었다. 하단 보드는 각각의 센서들의 통신을 진행하기 위한 커넥터 및 소자 등이 들어가 있으며 센서 값의 정확성을 높이기 위한 방법으로 격리(Isolation) 처리로 외부 노이즈에 영향을 받지 않는 보드를 설계하였다.
Fig. 5

2 STM32F407 Developing board

KSPE_2019_v36n12_1135_f005.jpg
3.2 제어 유닛 시스템 구성
Fig. 6은 제어 유닛을 설계 하기 위한 시스템 구성을 보여 준다. 그래픽 기반 시스템을 이용하여 개발 보드와 연결하여 각각의 센서 값들을 개발 보드를 통해 데이터를 주고 받으며 실시간으로 컨트롤이 가능하도록 구성을 하였다. PC와의 데이터를 주고받기 위한 방법으로 RS-232 통신을 사용하였다. 각각의 무빙스토퍼를 구동하는 서보 모터와 유량을 구동할 서보 모터의 통신 방법은 RS-422 통신 방법을 이용하였고 유량을 측정하는 유량 센서는 RS-485 Modbus RTU 마지막으로 가장 중요한 압력은 RS-232 방식으로 실시간으로 측정을 진행하였다.
Fig. 6

Block diagram for control system

KSPE_2019_v36n12_1135_f006.jpg
3.3 하드웨어 시스템 구성
Figs. 7과 8은 시스템 구성에 필요한 하드웨어이다. Fig. 7은 Servo HPLC 프렙컬럼의 전체 하드웨어 모식도로 컬럼을 서보모터로 기동하여 스크류 잭을 통해 무빙스토퍼를 컬럼 안으로 가압하는 방식의 장치로 구성되어있다. 컬럼 공정 시 슬러리 탱크 안에서 충진제 및 화학 물질이 컬럼 안으로 주입이 되어 내부에 상당한 압력이 발생된다. Fig. 8은 컬럼 가압을 진행후 액체를 이송시키는 펌프 시스템으로 고속의 유량을 지속적으로 흘려 크로마토그래피가 진행이 가능하도록 하는 시스템이다. 공정 종료후에는 솔벤트를 이송시켜 컬럼안을 세척하는 용도로도 사용된다.
Fig. 7

Development layout of servo HPLC prep column

KSPE_2019_v36n12_1135_f007.jpg
Fig. 8

Servo HPLC pump system

KSPE_2019_v36n12_1135_f008.jpg
Fig. 7은 실험에 사용한 U.I기반 시스템 운영 소프트웨어의 U.I이다. LabVIEW을 기반으로 하여 제작이 되었으며 하단부에는 압력 값과 유량 값을 실시간으로 측정된 값을 그래프적으로 구현을 하였으며 상단 부에 표시되어 있는 그래픽은 현재 HPLC 프렙컬럼의 진행 사항 들과 현재 돌아가는 AC 서보 모터의 Rpm 속도 등을 직관적으로 알고있게 개발되었다. 우측에 있는 표는 충전 변수를 기입 할 수 있는 Method 칸으로 구성이 되어 있다. 입력한 Method을 이용하여 자동화 운영이 가능한 운영 소프트웨어를 개발하였다.
Fig. 9는 개발된 서보 HPLC 프렙컬럼을 이용하여 특정 화학물 Lysozyme을 정제한 후 이론 단수를 측정한 그래프이다.
Fig. 9

Visual graphic software using LabVIEW

KSPE_2019_v36n12_1135_f009.jpg
Fig. 10

Result of column efficiency measure for lysozyme

KSPE_2019_v36n12_1135_f010.jpg
특정 화학물을 시료로 제조후 표준용액과 같이 개발한 서보 HPLC 프렙컬럼으로 분석을 진행 및 이론단수 측정하였다. 동일한 조건으로 총 10번의 실험을 진행 하여 공정 이 종료 후 이론 단수를 측정하여 재현성을 평가하였다. 10번의 모든 정제물의 순도는 95% 이상을 유지 하였다. 이러한 결과는 목표로 했던 95% 이상의 재현성을 달성하였다.
본 논문에서는 사용자가 직관적으로 HPLC 프렙컬럼 시스템을 사용하고 퍼지 추론을 이용하여 실시간 압력 제어를 진행하여 무빙스토퍼의 위치를 제어 그리고 재현성을 유지하는 자동화 기술을 개발하였다. 전체적인 공정의 진행은 Fig. 11과 같다. 이러한 자동화 기술의 개발은 분리된 화학 물질의 재현성 검사 시기존의 유압식 방식의 HPLC 프렙컬럼 보다 높은 재현성을 보여 주였으며 일반적인 사용자가 사용시 U.I 기반 시스템 운영소프트 웨어에 나와 있는 충전 변수 및 시작을 진행 하는 것 만으로도 숙련된 사용자와 동급 혹은 높은 성능을 보여 주었다. 실험 참가자의 설문조사를 통해서도 기존의 유압식 시스템보다 만족도가 높아졌다는 결론을 도출하는 것이 가능했다.
Fig. 11

Flow chart for HPLC prep column progress

KSPE_2019_v36n12_1135_f011.jpg
This work was supported by the Technological Innovation R&D Program (S2357158) funded by the Small and Medium Business Administration (SMBA, Korea)
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Oyoung Kwon
KSPE_2019_v36n12_1135_bf001.jpg
M.S & Ph.D. candidate in the Department of Mechanical Engineering, Korea University. His research interest is Field Robotics
Hyung Suk Park
KSPE_2019_v36n12_1135_bf002.jpg
Research Center Chief Youngjin Biochrom Co., Ltd. Mechanical Engineering.
Seok Geun Oh
KSPE_2019_v36n12_1135_bf003.jpg
Section Chief Youngjin Biochrom Co., Ltd. Signal processing & control engineering search interest is Machine.
Daehie Hong
KSPE_2019_v36n12_1135_bf004.jpg
Professor in the Department of Mechanical Engineering, Korea University. His research interest is Mechatronics & Field Robotics.

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Automation Control System for Servo Prep Column of High-Performance Liquid Chromatography Based on Fuzzy Inference Control
J. Korean Soc. Precis. Eng.. 2019;36(12):1135-1140.   Published online December 1, 2019
DOI: https://doi.org/10.7736/KSPE.2019.36.12.1135
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Automation Control System for Servo Prep Column of High-Performance Liquid Chromatography Based on Fuzzy Inference Control
J. Korean Soc. Precis. Eng.. 2019;36(12):1135-1140.   Published online December 1, 2019
DOI: https://doi.org/10.7736/KSPE.2019.36.12.1135
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Automation Control System for Servo Prep Column of High-Performance Liquid Chromatography Based on Fuzzy Inference Control
Image Image Image Image Image Image Image Image Image Image Image
Fig. 1 Hydraulic HPLC prep column (Young Jin Biochrom)
Fig. 2 Membership function of pressure
Fig. 3 Membership function of motor_rpm
Fig. 4 Membership function of moving stopper position
Fig. 5 2 STM32F407 Developing board
Fig. 6 Block diagram for control system
Fig. 7 Development layout of servo HPLC prep column
Fig. 8 Servo HPLC pump system
Fig. 9 Visual graphic software using LabVIEW
Fig. 10 Result of column efficiency measure for lysozyme
Fig. 11 Flow chart for HPLC prep column progress

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Automation Control System for Servo Prep Column of High-Performance Liquid Chromatography Based on Fuzzy Inference Control
PVL Pressure very low
PL Pressure low
PM Pressure middle
PH Pressure high
RVL Rpm very low
RL Rpm low
RM Rpm middle
RF Rpm fast
OT Over top
T Top
M Middle
IS Inner side
AND RVL RL RM RF
PVL None None T OT
PL None M T OT
PM IS M T None
PH IS M None None
No. P.V Data Discretion
1 Column inner 200 Input
2 Column height 367 Input
3 Packing density 0.52 Input
4 Filler 5,992 Output
5 Slurry 23.047 Output
6 Slurry height 734 Output
7 Column pressure 2.5 Input
8 Pump flow 1.884 Output
9 Stopper distance 611 Decision
10 Distance
Table 1 Linguistic variable
Table 2 Fuzzy rule
Table 3 HPLC charge variable

Table 1

Table 2

Table 3

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