The Fourth Industrial Revolution (Industry 4.0) brings a new chapter at dairy sector. Dairy 4.0 technologies are based on Big Data Analysis, Internet of Things, Robotics and Machine Learning. The usage of smart technologies to processing and analyzing complicated massive data has a significant impact in automation, optimization, functional costs and innovation. Artificial Intelligence tools are applied from dairy farms and production lines – including packaging- to supply chain. The aim of this paper is to demonstrate the most used applications of Machine Learning in dairy production so as to enhance the sustainability and the quality of dairy products. The most significant Machine Learning applications integrate machine vision, smart environmental sensors, activity collars, thermal imaging cameras, and digitized supply chain systems to facilitate inventory management. Challenges like milk adulteration, animal diseases, mastitis, traceability and supply chain losses are also addressed... [more]

Chemical process models often involve high non-linearity due to thermodynamic and kinetic relationships, with non-convex bilinear terms adding complexity to process optimization. Recently, data-driven models, particularly artificial neural networks (ANNs), have gained traction for representing chemical processing units. The predictive accuracy of ANNs depends on data quality, variable interactions, and network architecture, the latter being an optimization challenge itself. This study proposes and evaluates two strategies to optimize ANN architecture for modeling a propylene glycol production process from glycerol. The process includes a reactor and two distillation columns, with training data generated through simulation in Aspen Plus by varying design and operating variables. Two approaches are compared: random ANN structure generation and architecture optimization using the ant colony algorithm, a method suitable for discrete problems. Decision variables include the number of hidden... [more]

Artificial neural networks (ANNs) have gained popularity in the last years as tools to develop data-driven models of chemical process units. However, representing a system only with such artificial intelligence models may lead to a loss in the comprehension of the occurring phenomena. Hybrid models allow combining the predictive capabilities of ANNs with the foundational knowledge of rigorous models. This study explores the impact of hybridization in designing and optimizing shell-and-tube heat exchangers, comparing a full ANN-based model with a hybrid model. The hybrid model incorporates ANN predictions for highly nonlinear components, such as heat transfer coefficients, while other calculations are performed using the rigorous Bell-Delaware model. To generate the necessary data, the rigorous model is solved under randomly selected conditions. Using Python, one ANN predicts the exchanger's cost, while another predicts the heat transfer coefficients. Both models are optimized using the... [more]

Artificial neural networks are widely used as data-driven models for capturing complex, nonlinear systems. However, suboptimal training remains a significant challenge due to the nonlinearity of activation functions and the reliance on local solvers, which makes achieving global solutions difficult. One solution involves reformulating activation functions as piecewise linear approximations to convexify the problem, though this approach often requires substantial CPU time. This study demonstrates that a tailored branch-and-bound algorithm can effectively address these challenges by efficiently navigating the solution space using linear relaxations. The proposed method achieves minimal training error, offering a robust solution to the training bottleneck. Unlike traditional mixed-integer programming approaches, which often struggle to converge within reasonable CPU times, the SOSX algorithm shows superior scalability, with computational demand growing almost linearly rather than exponent... [more]

Predicting processes’ future behavior based on past data is vital for automatic control and dynamic optimization in engineering. Recent advances in deep learning, particularly Artificial Neural Networks, have improved predictions in various engineering fields. Recurrent Neural Networks (RNNs) are well-suited for time series data, as they naturally evolve through dynamic systems with recurrent updates. Despite their high predictive power, RNNs may underperform if their training ignores the model's future application. In Model Predictive Control, for example, the model evolves over time using only current information, relying on its own predictions at later steps. A model trained for one-step-ahead predictions may fail when tasked with multi-step-ahead forecasting in autoregressive mode. This study explores deep recurrent neural network models for predicting critical operational time series of a large-scale Electric Submersible Pump system. We present an innovative training approach, fra... [more]

The mathematical modeling of enzymatic reactors for esterification of fatty acids with sugars in the production of biosurfactants has been a useful tool for studying and optimizing the process. In particular, artificial neural networks and fuzzy systems emerge as promising methods for developing models for those processes. In this work, regarding artificial neural networks application, coupling of networks to reactor mass balances was considered in hybrid models to infer reactant concentrations over time. Computationally, an algorithm was constructed incorporating material balances, neural reaction rates, and step-by-step numerical integration (employing the classical Runge-Kutta method). Besides, based on an available set of experimental data, fuzzy logic was applied for modeling and optimization of the conversion of esterification as a function of operational process parameters (such as time, temperature and molar ratio of substrates). All computational development was carried out us... [more]