Optimization of energy consumption in cotton ginning enterprises using neural network method

. In the modern world, energy consumption optimization has become a critical concern across various industries due to environmental considerations and economic efficiency. Cotton ginning enterprises, which play a pivotal role in the textile supply chain, are no exception. This article explores applying neural network methods to optimize energy consumption in cotton ginning enterprises. We delve into the challenges faced by the industry, introduce the concept of neural networks, and discuss their potential to enhance energy efficiency. A case study demonstrates the practical implementation of the neural network approach in a cotton ginning setting, showcasing the potential benefits and providing insights into future directions for sustainable energy practices.


Introduction
Cotton ginning, the process of separating cotton fibers from their seeds, is a resource-intensive operation within the textile industry.Energy consumption is a significant cost factor for these enterprises, making energy optimization a crucial objective for both economic and environmental reasons.In recent years, advancements in machine learning techniques, particularly neural networks, have shown promise in tackling complex optimization challenges.This article explores the integration of neural network methods into the energy consumption optimization of cotton ginning enterprises.Early in the decade, research primarily centered on conventional optimization techniques for energy consumption in cotton ginning enterprises.Studies such as Ismail et al. [1] and Dube et al. [2] and explored process reengineering and control strategies to minimize energy usage.These studies highlighted the importance of improving mechanical efficiency and adopting energy-efficient technologies.
As machine learning gained prominence, researchers began investigating its applicability to energy optimization.Sahu et al. [3] introduced a predictive model using regression analysis to optimize energy consumption in cotton ginning processes.This marked an initial step toward incorporating data-driven techniques.
The mid-2010s saw a shift towards neural networkbased methods for energy optimization.Researchers recognized the ability of neural networks to capture intricate patterns in energy consumption data.Shanbeh et al. [4] introduced a study where neural networks were used to predict energy demand in ginning operations.This study laid the foundation for future research on the application of neural networks in the cotton ginning context.
As the decade progressed, researchers began exploring integrated systems and advanced algorithms to optimize energy consumption comprehensively.Fue et al. [5] proposed a model integrating artificial intelligence techniques, including neural networks, to optimize the entire ginning process.This holistic approach highlighted the potential of combining multiple strategies for enhanced results.
Towards the end of the decade, attention shifted towards practical implementation and case studies.Hardin et al. [6] presented a case study on a real-world cotton ginning plant, demonstrating the effectiveness of a neural network-based predictive model for energy optimization.This study provided valuable insights into the feasibility and benefits of implementing such methods in actual industry settings.
While significant progress was made during the decade, challenges remained.Data availability, model interpretability, and scalability were identified as key issues.As highlighted by Patel et al. (2020), efforts were needed to develop more robust neural network architectures specifically tailored to the cotton ginning industry.
The years between 2010 and 2023 marked a significant evolution in the approach to energy consumption optimization in cotton ginning enterprises.From traditional methods to the emergence of neural network-based techniques, researchers demonstrated the potential of data-driven approaches in enhancing energy efficiency.Case studies and integrated systems showcased the applicability of these techniques in real-world scenarios [7,8].As the decade concluded, the stage was set for future research to address challenges and continue refining these methods for sustainable energy practices in cotton ginning enterprises.
This project is aimed at optimizing energy consumption in cotton ginning enterprises through the utilization of neural network methodologies.

Materials and Methods
Historical energy consumption data from diverse cotton ginning enterprises were gathered to form the basis for this study.The dataset encompassed a comprehensive array of ginning processes, encompassing distinct operational conditions and energy usage patterns.
In this research, a Bayesian neural network (BNN) architecture was adopted to optimize energy consumption in cotton ginning enterprises.The BNN framework integrates uncertainty estimation, offering a more comprehensive understanding of predictive model outcomes.The network comprised an input layer, multiple hidden layers, and an output layer.Notably, Bayesian principles were embedded into the network's structure to provide probabilistic predictions.
The Levenberg-Marquardt algorithm, renowned for its convergence speed in non-linear optimization problems, was employed for training the Bayesian neural network.This algorithm adjusts the neural network's weights by minimizing the difference between predicted and actual energy consumption values while considering the network's uncertainty estimates.
A feedforward neural network architecture was chosen for its ability to capture complex relationships within the data.The network consisted of an input layer, hidden layers, and an output layer.The number of neurons in the hidden layers was determined through experimentation to achieve optimal performance.
Relevant operational parameters such as ginning speed, humidity levels, and machine configurations were selected as input features.These features were normalized to ensure consistent scaling within the neural network.

Training and Validation
The dataset was divided into training and validation subsets.The neural network was trained using backpropagation, a gradient-based optimization technique.Mean Squared Error (MSE) was employed as the loss function to minimize the difference between predicted and actual energy consumption values.
The training of the Bayesian neural network (BNN) commenced with the initialization of weights and biases in the network's architecture.The Levenberg-Marquardt optimization algorithm was then employed to iteratively adjust these weights to minimize the difference between the predicted and actual energy consumption values while accounting for the uncertainty estimates.The Levenberg-Marquardt algorithm incorporates the curvature of the error surface, facilitating convergence even in the presence of non-linearities.

Bayesian Uncertainty Estimation
Integral to the training process of the BNN was the incorporation of Bayesian uncertainty estimation.This entailed the assignment of probability distributions to the weights and biases of the neural network, effectively quantifying the uncertainty associated with each parameter.By considering these uncertainties, the BNN not only produced point predictions but also provided a probabilistic range of predictions, allowing for a more comprehensive understanding of the model's confidence in its output.

Loss Function for Training
The objective of training was to minimize the negative log-likelihood of the observed energy consumption given the predicted energy consumption and its associated uncertainty.The loss function, denoted as L, was defined as follows: Where: N is the number of data points; y actual,i is the actual energy consumption value for the ith data point; y predicted,i is the predicted energy consumption value for the ith data point; σ i is the uncertainty estimate for the ith data point.
The optimization process aimed to minimize this loss function by adjusting the weights and biases of the BNN.

Validation and Performance Metrics
To assess the trained Bayesian neural network's performance, a separate validation dataset was employed.The model's predictions were compared to the actual energy consumption values in this dataset.The following performance metrics were calculated: Mean Absolute Error (MAE) Root Mean Squared Error (RMSE) The solution to this problem is a very complicated and time-consuming process.The technological processes of the considered cotton ginning enterprise are considered to be very complex and difficult.A prediction algorithm based on a common method of correlation and regression analysis consists of a mathematical description.
Planned consumption of electricity: where  -rate of electricity consumption per product unit for per month, kW•h/t;  -planned cost of production, t.
The resolution to these challenges could potentially involve the creation and application of a predictive framework for estimating electric power usage, utilizing artificial neural networks (ANNs).This predictive technique relies on employing multilayered artificial neural networks organized in a sequential configuration.ANNs represent a computational paradigm inspired by basic biological mechanisms reminiscent of human brain functions.A fundamental unit within these architectures is an artificial neuron, denoted for its likeness to its biological counterpart.
The training and prediction of the artificial neural network (ANN) involve utilizing input data from the commercial electricity accounting automated system's database.This dataset is complemented by additional parameters specific to the electricity consumption of each enterprise, such as year days, air temperature, humidity, atmospheric pressure, days of the week, and time of day.Tailoring individual predictions for each entity involves the crucial task of discerning pertinent variables while disregarding extraneous ones.To address this, the ANN employs the Bayesian regularization method, as depicted in Figure 1.This technique guides the neuron's training to align with network operations, enabling the determination of weight coefficients.
where М -exact neural network model;  |, this is the previous density, previous weights information;  |, ,  -is a probability function that is the probability that the data will occur given the weights;  |, ,  -normalization factor, which guarantees that the total probability is equal to 1 [11] Depending on the Gaussian noise, there is a probability w of the given parameters  |, , ,  , , here  ,    .Within this Bayesian context, the optimal weights are sought to optimize the conditional likelihood.
Figure 4 displays the outcome of the training process for the neural network constructed using the Bayesian regularization technique.The results obtained reveal that the ANN approach exhibits substantial promise in crafting predictive models aimed at enhancing precision.The optimal predictive model is characterized by straightforward training, rapid development, flexibility in accommodating environmental shifts, convenient adaptability, and consistent performance even within unstable conditions.
2. Consequently, the integration of this method alongside prediction systems based on ANNs presents a substantial opportunity to curtail expenses associated with fines arising from discrepancies between ordered and actual electricity consumption.To achieve this objective, we confined ourselves to mathematical representations of the neural network.As a consequence, significant enhancements in prediction accuracy can be achieved through the adoption of this technique.

Figure 1 .
Figure 1.Neural Network Architecture Figures 2 and 3 illustrate the outcome of the assessment of indicator correlations using the ANN approach.The findings demonstrate the existence of notable and moderate connections between input and output indicators.

Figure 4 .
Figure 4. Evolution of neural network regression training over 1000 epochs, attaining maximum iteration Figure 5 depict the training distribution error and the time series output element outcome.

Figure 5 .
Figure 5.Time Series Output Element Outcome