Abstract
The high electricity and water consumption in industrial textile dyeing processes represents an environmental and economic challenge, requiring optimization strategies to reduce costs and impacts toward cleaner production. This work proposes an optimization model to minimize costs associated with water and electricity consumption in industrial textile dyeing processes. The model has a Mixed Integer Linear Programming (MILP) formulation. The objective function to be minimized is the total process costs. The constraints consider production capacity, daily production limits, and specific costs per material. A case study was conducted in a real industrial process for three types of tissue: cotton, polyester, and polyamide. The model was coded in GAMS and the CPLEX solver was used to solve the problem. The results showed that water consumption accounted for 78.2% of the total cost in the optimal solution. Using the same model, an alternative simulation was performed, replacing four smaller-capacity machines with a single larger-capacity machine, resulting in a marginal reduction in total costs. Simulations were also performed to replace the current machines with highly efficient automated HT (High Temperature) machines, indicating a potential 71.39% reduction in water consumption costs. The conclusion is that the proposed model is effective for optimizing textile dyeing processes, balancing operational efficiency and sustainability, and is applicable in complex industrial scenarios.