Considering the exponential growth of today’s industry and the wastewater results of its processes, it needs to have an optimal treatment system for such effluent waters to mitigate the environmental impact generated by its discharges and comply with the environmental regulatory standards that are progressively increasing their demand. This leads to the need to innovate in the control and management information systems of the systems responsible to treat these residual waters in search of improvement. This paper proposes the development of an intelligent system that uses the data from the process and makes a prediction of its behavior to provide support in decision making related to the operation of the wastewater treatment plant (WWTP). To carry out the development of this system, a multilayer perceptron neural network with 2 hidden layers and 22 neurons each is implemented, together with process variable analysis, time-series decomposition, correlation and autocorrelation techniques; it is possible to predict the chemical oxygen demand (COD) at the input of the bioreactor with a one-day window and a mean absolute percentage error (MAPE) of 10.8%, which places this work between the adequate ranges proposed in the literature.

Long-range wide-area network (LoRaWAN) technology can be a good candidate for industrial applications in the Internet of Things and adaptive data rate (ADR) algorithms help to improve LoRaWAN performance. These algorithms compute a link budget to define the data rate and the transmission power. However, the performance of these algorithms must be evaluated in industrial environments that pose challenging conditions to wireless communications due to obstacles, interference, and movement. Therefore, this article compares the performance of LoRaWAN with six different ADR algorithms using a realistic propagation model from an industrial environment. This research uses the framework for LoRa (FLoRa) environment in objective modular network testbed in C++ (Omnet++) to compare the packet delivery ratio and energy consumption with different gateway locations and data traffic. Results show that the algorithms that decrease the link budget in poor channel conditions have better performance in industrial scenarios. Furthermore, overestimating the channel behavior decreases packet delivery.

To reduce demand and discharge, instead of industrial wastewater being not enough treated and disposed of, it can be recycled, reused, or recovered considering well management. Thus, having a substantial decrease in the water need and environmental impacts. The challenge is to select the proper combination of processes based on the wastewater quality and budget limitations. Therefore, we aimed to provide a distinctive and comprehensive perspective on the current state of industrial wastewater treatment technologies by giving a complete overview of their major advantages, disadvantages, and upcoming challenges in the context of the circular economy. This study considered 198 peer-reviewed scientific publications from the Scopus and Google Scholar databases, mostly from the last 5 years. For this, specific search terms and Boolean Operators were applied as inclusion and exclusion criteria. Up to 57 different technologies, from conventional to advanced, were reviewed for reliable and sustainable industrial wastewater treatment and derived sludges. We evidence a vast potential to exploit and make them viable for the circular economy, even in the mature ones. Overall, we identify that regardless of the alternative, in particular those not vastly implemented, future investigations should prioritize: (1) The capacity to remove selected pollutants and decrease impurities; (2) energy recovery and efficiency; (3) environmental safety; (4) economic viability and industrial implementation; (5) hybridization; and (6) sustainability through waste processing. GRAPHICAL ABSTRACT

An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area.