AUTOMATION OF THE CHEWING GUM PRODUCTION PROCESS

Abstract

Purpose. The aim of the study is to design, model, and evaluate a comprehensive automation solution for the chewing gum production process using a distributed control system architecture based on the Siemens SIMATIC PCS 7 platform. The research targets the elimination of key limitations inherent in traditional manufacturing, namely inter-batch variability, low dosing accuracy, and insufficient process traceability.

Methodology. A systems engineering approach was applied to analyse and automate the technological process of chewing gum production. The process was decomposed into five key unit operations: powder conveying, liquid dosing, high-shear mixing, cooling, and extrusion. Input, manipulated, and output variables were identified for each operation. The control system architecture was designed on the basis of Siemens SIMATIC PCS 7, integrating programmable logic controllers for real-time equipment management and a SCADA system for process visualisation, recipe management, and data logging. Control logic validation and batch cycle time optimisation were conducted in the MATLAB Simulink environment by simulating material flows, mixer thermal dynamics, and sensor and actuator behaviour.

Findings. The designed automated system demonstrated a theoretical reduction in dosing error to ±0.2% for macro-ingredients and ±0.5% for micro-ingredients, representing a 90–97% reduction compared to manual methods. The coefficient of variation for critical product properties is projected to decrease from 8–15% (manual production) to 3–5%. The three-stage adaptive mixing strategy based on torque monitoring reduces mixing duration by 15–20%, while parallel PLC-managed dosing reduces the dosing phase by approximately 40%. Overall batch cycle time is reduced by 20–25%. The SCADA interface provides complete electronic traceability with all process parameters logged at 1 sample per second. Recipe changeover time is reduced from 30-60 minutes to 5–10 minutes via a recipe selection menu.

Originality. The integrated automated control system architecture covering the full chewing gum production cycle – from raw material intake to extrusion – has been developed and validated by computer simulation. An adaptive mixing control algorithm based on torque feedback has been proposed, ensuring that the target rheological state is achieved regardless of variations in raw material properties.

Practical value. The proposed automation model provides a scalable framework for confectionery manufacturers, suitable both for modernising existing production lines and for designing new facilities. Implementation of the system ensures improved product quality, reduced operational costs, enhanced manufacturing flexibility, and compliance with HACCP and GMP requirements through automatic electronic batch record-keeping. The system conforms to the ANSI/ISA-88.00.01-2010 standard and can be further extended through the integration of machine learning technologies for predictive control and maintenance.