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Crystallization Process in the Sugar Industry: A Discussion On Fundamentals, Industrial Practices, Modeling, Estimation and Control

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Abstract

The sugar industry plays a crucial role in numerous economies worldwide, and projections indicate an increase in sugar demand in the coming years. Sugar manufacturing is a complex and highly energy-demanding process, encompassing various sub-processes, including the milling of sugarcane, clarification of raw cane juice, evaporation of syrup, crystallization of syrup, and centrifugation. The crystallization process involves extracting solid sucrose crystals from a supersaturated solution, with supersaturation being a crucial variable. Most previous review articles have focused on specific topics of the crystallization process in the sugar industry. This review aims to provide a broader and updated discussion. It explores various aspects, such as the fundamentals of the process in the sugar industry, a technological description of the three-stage operation, and an analysis of the role of supersaturation. The review examines the main process variables, the most commonly used industrial sensors, and their limitations. Additionally, it discusses the main proposals and approaches found in the literature related to monitoring, modeling, and control of crystallization in the sugar industry. The article identifies and analyzes some limitations present in the literature, including the selection of instruments for industrial monitoring, confusing references to supersaturation sensors, and the need for proper error analysis in the design of process-focused estimators. In particular, there is an emphasis on including information about error bounds for supersaturation estimators.

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Data Availability

No datasets were generated or analysed during the current study.

Abbreviations

A :

Ash content

a, b, c :

mother liquor parameters

AM :

Averaged crystal size (mm)

B :

Mass fraction of dry substance (\(^\circ\)bx)

c :

concentration

cc :

crystal content (\(\%\))

CV :

coeficient of variation (\(\%\))

\(c_{sol}\) :

solubility coefficient

\(d_v\) :

volume increases

\(D_g\) :

growth rate dispersion (mm\(^{2}/\)s)

F :

flow rate (m\(^{3}/\)s)

G :

crystal growth rate (mm/s)

J :

crystallization rate (kg/s)

L :

crystals length (mm)

m :

mass (kg)

\(\dot{m}\) :

mass flow rate (kg/s)

N :

number of crystals

n :

crystal population density

P :

purity (mass fraction of sugar \(\%\))

p :

pressure (bar)

\(q_{s/w}\) :

Sucrose to water ratio for supersaturated solutions (g/g)

\(q_{ns/w}\) :

non sugar/water ratio (g/g)

\(q_{s/w,sat}\) :

Sucrose to water ratio for saturated pure solutions (Solubility) (g/g)

\(S\!S\) :

Supersaturation

\(S\!S_{cr}\) :

Limit Supersaturation

T :

temperature (\(^\circ\)C)

t :

time (s)

\(\alpha _c\) :

crystal parameter

\(\kappa\) :

conductivity (\(\%\))

\(\eta\) :

viscosity

\(\mu _j\) :

Distribution moment

\(\rho\) :

density (kg/m\(^{3}\))

i :

impurities

f :

feed

sat :

saturation

s :

dissolved sucrose

c :

crystal

w :

water

cw :

condensate

hs :

heat steam

ml :

mother liquor

mg :

magma

vap :

emmited vapor

ATR:

Attenuated Total Reflection

ASTME:

Time Series Analysis

ANN:

Artificial Neural Network

BDPCA:

Batch Dynamic PCA

BPR:

Boiling Point Rise

CSD:

Crystal Size Distribution

DDSS:

Data Driven Soft-sensors

FTIR:

Fourier transform infrared

HTM:

Heat and mass Transfer

HSS:

Hybrid Soft-sensors

IA:

Image Analysis

KBH:

Knowledge-based hybrid

KP:

Kinetic parameters

LF:

Fuzzy Logic

MBSS:

Model Based Soft-sensors

MSE:

Mean square error

MEP:

Mass, energy and population balance

ME:

Mass and energy balance

MP:

Mass population balance

MoEq:

From Moment Equations

MWPCA:

Moving Window PCA

P[MYAMP:

ID] Pipe and Instrument Diagram

PID:

Proportional-Integral Derivative Controller

PLC:

Programmable Logic Controller

PCA:

Principal Component Analysis

PLS:

Partial Least Squares

RTD:

Resistance Temperature Detector

SVM:

Support Vector Machines

TSVR:

Twin Support Vector Regression

UKF:

Unscented Kalman Filter

MPC:

Model-Based Predictive Control

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  1. Instituto de Automática, UNSJ-CONICET, Ave. San Martín (Oeste) 1109, Argentina, J5400ARL, San Juan, Argentina

    Humberto Morales, Fernando di Sciascio, Estefania Aguirre-Zapata & Adriana Amicarelli

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  1. Humberto Morales
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  2. Fernando di Sciascio
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  3. Estefania Aguirre-Zapata
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  4. Adriana Amicarelli
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Contributions

Humberto Morales: Conceptualization, Formal analysis, Writing - Original Draft, Investigation. Fernando di Sciascio: Methodology, Formal Analysis, Writing - Review, Editing, Investigation, Project Administration. Estefania Aguirre-Zapata: Formal Analysis, Writing - Review, Editing, Investigation. Adriana N. Amicarelli: Investigation, Conceptualization, Writing - Review, Supervision, Project Administration.

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Morales, H., di Sciascio, F., Aguirre-Zapata, E. et al. Crystallization Process in the Sugar Industry: A Discussion On Fundamentals, Industrial Practices, Modeling, Estimation and Control. Food Eng Rev (2024). https://doi.org/10.1007/s12393-024-09377-3

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