Authors of this paper take under investigation the optimization of biological processes during the wastewater treatment in sequencing batch reactor (SBR) plant. A designed optimizing supervisory controller generates the dissolved oxygen (DO) trajectory for the lower level parts of the hierarchical control system. Proper adjustment of this element has an essential impact on the efficiency of the wastewater treatment process as well as on the costs generated by the plant, especially by the aeration system. The main goal of the presented solution is to reduce the plant energy consumption and to maintain the quality of effluent in compliance with the water-law permit. Since the optimization is nonlinear and includes variations of different types of variables, to solve the given problem the authors performed simulation tests and decided to implement a hybrid of two different optimization algorithms: artificial bee colony (ABC) and direct search algorithm (DSA). Simulation tests for the wastewater treatment plant case study are presented.

Illegal, Unreported, and Unregulated fishing is a major threat to human food supply and marine ecosystem health. Not only is it a cause of significant economic loss but also its effects have serious long-term environmental implications, such as overfishing and ocean pollution. The beginning of the fight against this problem dates since the early 2000s. From that time, a number of approaches and methods have been developed and reported. A key role in this topic is played by machine learning algorithms which exploit data provided by classical and high-tech sensors, devices and systems such as for example: CCTV, on-board cameras placed on autonomous vehicles, Global Positioning Systems, radars, Automatic Identification Systems, Vessel Monitoring Systems, or Coastal Surveillance Systems. The main objective of this paper is to provide the reader with knowledge about the scale of this phenomenon, methods to tackle the issue, and the current state of research on the subject. This has been achieved through a review of existing approaches that deal with these harmful phenomena by using dedicated artificial intelligence and machine learning tools, as well as the accompanying equipment and devices. In addition, flaws and gaps in current methods, and future directions are disscussed.

The paper presents the complete design processes of novel aeration control systems in the SBR (sequencing batch reactor) wastewater treatment plant (WWTP). Due to large energy expense and a high influence on biological processes, the aeration system plays a key role in WWTP operation. The paper considers the aeration system for a biological WWTP located in the northeast of Poland. This system consists of blowers, the main collector pipeline, three aeration lines with different diameters and lengths, and diffusers. Classical control systems applied for this type of installation are based on PID (proportional–integral–derivative) controllers, the settings of which are often found experimentally. The article presents the optimization of these settings and the design of an alternative control algorithm—the fuzzy controller.

The paper proposes the design of the supervisory heuristic fuzzy control system applied to a Sequencing Batch Reactor (SBR) in the Wastewater Treatment Plant (WWTP). Based on real data records and the biological process and aeration system models, a control system for biological processes has been designed. The supervisory control system performs management of the reactor work cycle, determines the phase length, controls the sludge age, calculates the setpoint of dissolved oxygen, and adapts parameters of the lower control layer. The results of simulation tests for the case study wastewater treatment plant are presented.