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.

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

This paper presents an energetically autonomous IoT sensor powered via thermoelectric harvesting. The operation of thermal harvesting is based on maintaining a temperature gradient of at least 26.31 K between the thermoelectric-generator sides. While the hot side employs a metal plate, the cold side is attached with a phase-change material acting as an effective passive dissipative material. The desired temperature gradient allows claiming power conversion efficiencies of about 26.43%, without efficiency reductions associated with heating and soiling. This work presents the characterization of a low-cost off-the-shelf thermoelectric generator that allows estimating the production of at least 407.3 mW corresponding to 2.44 Wh of available energy considering specific operation hours—determined statistically for a given geographic location. Then, the energy production is experimentally verified with the construction of an outdoor IoT sensor powered by a passively-cooled thermoelectric generator. The prototype contains a low-power microcontroller, environmental sensors, and a low-power radio to report selected environmental variables to a central node. This work shows that the proposed supply mechanism provides sufficient energy for continuous operation even during times with no solar resource through an on-board Li-Po battery. Such a battery can be recharged once the solar radiation is available without compromising sensor operation.

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.

Purpose: The purpose of this paper is to provide a comprehensive methodology and a case study about the successful integration of FCA with continuous improvement tools for strategic decision-making processes. Reliable knowledge of the condition of tangible assets and their ability to fulfill their target activities over time are required for an assertive strategical decision process. Facility condition assessment (FCA) is a recognized methodology that allows the systematic evaluation of this performance. For those companies whose primary objective is the production of goods, decisions associated with improvements on the productive system or re-adaptation of existing assets may also require the implementation of alternative methodologies, with a direct impact on the indicators of the company and therefore on the FCA. Design/methodology/approach: This study presents a methodology for the integration of FCA and lean manufacturing (LM) as a tool in strategic decision-making process that involves the integration of continuous improvement processes or significant changes in the production process, in which the condition of the installation impacts decisively the productivity of the system. Findings: The results of the implementation on an insecticide and herbicide production plant indicate an increase of 33 per cent in the capacity of the formulation process and over 20 per cent reduction in the internal quality claims associated with the packaging system. Practical implications: Those methodological stages are applicable to facilities in which the FCA shows the need for significant reconditioning of assets, the need to increase the efficiency and/or the production capacity. This methodology integrates elements of continuous improvement and redesign of production systems. Originality/value: The original value of this paper is oriented to the capacity to integrate different FCA and LM tools through the company indicators of productivity key performance indicators and, in addition, of a comprehensive illustration based on a study case.

This paper describes the analysis and design of a TEH prototype for powering up outdoor sensors and devices. The device employs the heat gradient developed between a metal plate that concentrates the solar radiation and a dissipative material. First, TEG modeling is presented to favor the estimation of the available power; next, a design of experiments is carried out to determine the maximum solar radiation during a day. Then, a performance analysis demonstrates conversion efficiencies higher that 20%, which surpasses the efficiency of any commercially available solar panel; while keeping the same superficial area of only 16cm ² . Thus, given such performance metric, a complete power unit is devised complementing the TEG operation with a 3.7V 550mAh Li-Po battery. The complete unit is able to provide enough energy to power up an Arduino-like based sensing unit and charge an on-board battery guaranteeing in that way a continuous operation. The total energy available from the prototype allows maintaining a battery discharge percentage of 50% when powering up a maximum load of 133.61mW during selected ranges.