The treatment processes from a wastewater treatment plant (WWTP) are known for their complexity and highly nonlinear behavior, which makes them challenging to analyze, model, and especially, to control. This research studies how machine learning (ML) with a focus on deep learning (DL) techniques can be applied to optimize the treatment processes of WWTPs, highlighting those case studies that propose ML and DL methods that directly address this issue. This research aims to study the ML and DL systematic applications in optimizing the wastewater treatment processes from an industrial plant, such as the modeling of complex physical–chemical processes, real-time monitoring and prediction of critical wastewater quality indicators, chemical reactants consumption reduction, minimization of plant energy consumption, plant effluent quality prediction, development of data-driven type models as support in the decision-making process, etc. To perform a detailed analysis, 87 articles were included from an initial set of 324, using criteria such as wastewater combined with ML, DL, and artificial intelligence (AI), for articles from 2010 or newer. From the initial set of 324 scientific articles, 300 were identified using Litmaps, obtained from five important scientific databases, all focusing on addressing the specific problem proposed for investigation. Thus, this paper identifies gaps in the current research, discusses ML and DL algorithms in the context of optimizing wastewater treatment processes, and identifies future directions for optimizing these processes through data-driven methods. As opposed to traditional models, IA models (ML, DL, hybrid and ensemble models, digital twin, IoT, etc.) demonstrated significant advantages in wastewater quality indicator prediction and forecasting, in energy consumption forecasting, in temporal pattern recognition, and in optimal interpretability for normative compliance. Integrating advanced ML and DL technologies into the various processes involved in wastewater treatment improves the plant systems’ predictive capabilities and ensures a higher level of compliance with environmental standards.

Increasing environmental concerns and the need for sustainable materials have driven a focus towards the utilization of recycled polylactic acid (PLA) in additive manufacturing as PLA offers advantages over other thermoplastics, including biodegradability, ease of processing, and a lower environmental impact during production. This study explores the optimization of the mechanical properties of recycled PLA parts through a combination of experimental and machine learning approaches. A series of experiments were conducted to investigate the impact of various processing parameters, such as layer thickness and infill density, as well as annealing conditions, on the mechanical properties of recycled PLA parts. Machine learning algorithms have proven the possibility to predict tensile behavior with an average error of 6.059%. The results demonstrate that specific combinations of processing parameters and post-treatment annealing differently improve the mechanical properties (with 7.31% in ultimate tensile strength (UTS), 0.28% in Young’s modulus, and 3.68% in elongation) and crystallinity (with 22.33%) of recycled PLA according to XRD analysis, making it a viable alternative to virgin PLA in various applications such as sustainable packaging solutions, including biodegradable containers, clamshell packaging, and protective inserts. The optimized recycled PLA parts exhibited mechanical properties and crystallinity levels comparable to those of their virgin counterparts, highlighting their potential for reducing environmental impact and saving costs. For both as-built and annealed samples, the optimal settings for achieving high composite desirability involved a 0.2 mm layer thickness, with 75% infill for the as-built samples and 100% infill for the annealed samples. This study provides a comprehensive framework for optimizing recycled PLA in additive manufacturing, contributing to the advancement of sustainable material engineering and the circular economy.

Control systems behavior can be analyzed taking into account a large number of parameters: performances, reliability, availability, security. Each control system presents various security vulnerabilities that affect in lower or higher measure its functioning. In this paper the authors present a method to assess the impact of security issues on the systems availability. A fuzzy model for estimating the availability of the system based on the security level and achieved availability coefficient (depending on MTBF and MTR) is developed and described. The results of the fuzzy inference system (FIS) are presented in the last section of the paper.

The paper focuses on the conceptual development of an innovative method for authenticating sensors connected using Modbus TCP. Modbus TCP is a highly used industrial networking protocol that has very good performances but lacks security. In the first section of the paper, the authors try to highlight the importance of security of industrial control systems. The second section is dedicated to presenting the authentication process and the state-of-the-art regarding Modbus devices authentication. The third section focuses on presenting the characteristics of Modbus TCP protocol. The last section discusses the proposed conceptual solution showing a possible method of implementation.

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The objective of this paper is to test two sceneries of multicast transmissions through the WiMAX network and identify the optimal transmission method. The first method is a simulation of the WiMAX networks in a network simulator (Network Simulator 3 - NS3) using the standard NS3 libraries. The second simulation implemented in NS3 as well, contains the code altered in a way that will allow implementing the RLNC (random linear network coding) method.