The textile industry, a substantial component of the global economy, holds significant importance due to its environmental impacts. Particularly, the use of water and chemicals during dyeing processes raises concerns in the context of climate change and environmental sustainability. Hence, it is crucial from both environmental and economic standpoints for textile factories to adopt green industry standards, particularly in their dyeing operations. Adapting to the green industry aims to reduce water and energy consumption in textile dyeing processes, minimize waste, and decrease the carbon footprint. This approach has become crucial in achieving sustainability in textiles following the signing of the Paris Climate Agreement. Important elements of this transformation include the reuse of washing waters used in the dyeing process, the recycling of wastewater, and the enhancement of energy efficiency through necessary methodological and equipment changes. This study analyzes the energy, labor, production, and consumption data since 2011 for a textile factories with four branches located in the Adana Organized Industrial Zone. Among these factories, the one designated as UT1, which has the highest average energy and water consumption compared to the other three branches, is selected. In recent years, the use of artificial intelligence and machine learning technologies in predicting industrial processes has been increasingly observed. The data are analyzed using LSTM (Long Short-Term Memory) and ANN (Artificial Neural Networks) forecasting methods. Particularly, the LSTM algorithms, which provided the most accurate results, have enabled advanced forecasting of electricity consumption in dyeing processes for future years. In 2020, electricity consumption was recorded as 3,717,224 kWh and this consumption was reflected in the total energy cost as TRY 1,916,032. Electricity consumption accounts for 22.34% of total energy consumption, while the share of this energy type in the cost is 43.25%. In the light of these data, the MAPE value for energy consumption forecasts using the LSTM model was 0.45%, which shows that the model is able to forecast with high accuracy. As a result, a solar power plant was installed to optimize energy consumption, and in 2023 60% energy savings were achieved in summer and 25% in winter. The electricity consumption forecasting results have been an essential guide in planning strategic initiatives to enhance factory efficiency. Following improvement efforts aimed at reducing energy consumption and lowering the carbon footprint, significant optimizations in processes and layouts have been made at specific bottleneck points within the facility. These improvements have led to savings in labor, time, and space, and have reduced unit production costs.

The Carbon Border Adjustment Mechanism was developed to ensure that industrial sectors operating outside the EU follow the same environmental standards and targets while competing with the EU’s carbon market. This mechanism aims to calculate the carbon footprint of goods and services imported into the EU and make carbon adjustments accordingly. The transition phase, starting in 2023, represents the period when the Carbon Border Adjustment Mechanism will be implemented. The completion of the transition phase is targeted for 2025. By this date, the effective implementation of this mechanism is aimed at demonstrating that countries outside of the EU comply with emissions regulations using Carbon at Border certificates. The textile industry’s products have a significant environmental impact throughout their life cycle, from the production of raw materials to the disposal of the finished product. Textile production, especially synthetic yarns, requires large amounts of energy, contributing to greenhouse gas emissions and climate change. In this study, a “cradle-to-customer plus waste” life cycle assessment (LCA) is conducted to evaluate the environmental impacts of two products in the textile sector. The Monte Carlo analysis method can be used to handle uncertainties in LCA calculations. It is a method for modeling uncertainties and statistically evaluating results. In this study, this method is preferred at the stage of determining uncertainties. The processes from chips to yarns are investigated for two synthetic yarns: polyester (PES) and polypropylene (PP). The carbon emissions of PP and PES used in textiles are calculated for the first time in this study using detailed modeling with LCAs and a real application. The main production operations are considered: (i) transport of raw materials and packaging material, (ii) energy consumption during the production process, (iii) transport of products, and (iv) end-of-life steps. When the actual data obtained from a company are analyzed, the carbon footprints (CFs) of the PES and PP are calculated to be 13.40 t CO₂-eq (t PES)-1 and 6.42 t CO₂-eq (t PP)-1, respectively. These data can be used as reference points for future studies and comparisons. According to the results obtained, when the energy consumption and raw material stages in the production of the PES and PP products are compared, it is seen that the CF of PP yarn is lower, and it is more environmentally friendly. These findings can be utilized to enhance government policies aimed at reducing greenhouse gas emissions and managing synthetic yarn production in Türkiye. Since PP and PES raw materials are predominantly used in synthetic yarns, this study’s objective is to quantify the carbon emissions associated with the utilization of these raw materials and provide guidance to companies engaged in their production.

In order to produce more efficient, sustainable-clean energy, accurate prediction of wind turbine design parameters provide to work the system efficiency at the maximum level. For this purpose, this paper appears with the aim of obtaining the optimum prediction of the turbine parameter efficiently. Firstly, the motivation to achieve an accurate wind turbine design is presented with the analysis of three different models based on artificial neural networks comparatively given for maximum energy production. It is followed by the implementation of wind turbine model and hybrid models developed by using both neural network and optimization models. In this study, the ANN-FA hybrid structure model is firstly used and also ANN coefficients are trained by FA to give a new approach in literature for wind turbine parameters’ estimation. The main contribution of this paper is that seven important wind turbine parameters are predicted. Aiming to fill the mentioned research gap, this paper outlines combined forecasting turbine design approaches and presents wind turbine performance in detail. Furthermore, the present study also points out the possible further research directions of combined techniques so as to help researchers in the field develop more effective wind turbine design according to geographical conditions.

Energy is one of the most fundamental elements of today’s economy. It is becoming more important day by day with technological developments. In order to plan the energy policies of the countries and to prevent the climate change crisis, CO₂ emissions must be under control. For this reason, the estimation of CO₂ emissions has become an important factor for researchers and scientists. In this study, a new hybrid method was developed using optimization methods. The Shuffled Frog-Leaping Algorithm (SFLA) algorithm has recently become the preferred method for solving many optimization problems. SFLA, a swarm-based heuristic method, was developed in this study using the Levy flight method. Thus, the speed of reaching the optimum result of the algorithm has been improved. This method, which was developed later, was used in a hybrid structure of the Firefly Algorithm (FA). In the next step, a new Artificial Neural Network (ANN)-based estimation method is proposed using the hybrid optimization method. The method was used to estimate the amount of CO₂ emissions in Türkiye. The proposed hybrid model had the RMSE error 5.1107 and the R2 0.9904 for a testing dataset, respectively. In the last stage, Türkiye’s future CO₂ emission estimation is examined in three different scenarios. The obtained results show that the proposed estimation method can be successfully applied in areas requiring future estimation.

Today, in parallel with the population growth and the advancement of technology, development concerns have started to arise in terms of country administrators. Therefore, alternative solutions to classical energy sources are sought. Renewable energy sources are one of the preferred energy sources today. The popularity of renewable energy sources, including solar energy, is increasing day by day. Solar energy has the potential and accessibility to spread faster than other renewable energy sources. Since Türkiye is located in a region with a high potential in terms of solar energy, which is generally called the sun belt, it is a right decision to prefer solar energy as an energy source in our region. In this study, time series prediction using Long Short-Term Memory (LSTM) Network method is used for short-term solar irradiance estimation. In order to demonstrate the success of the results, a comparison was made with the Artificial Neural Network (ANN) method. Finally, prediction results of solar irradiance were compared with statistical tests and error analyzes were given in numerically.