Environmental protection is a priority of the economic and social development that aims at obtaining a clean and healthy environment that does not affect the development possibilities of future generations. It is, therefore, imperative to ensure the protection of the environment and the conservation of natural resources, especially water, according to the requirements of the sustainable economic and social development. Deterioration of the environment involves alteration of the physico-chemical and structural characteristics of the natural components of the environment, reduction of diversity or biological productivity of natural ecosystems, damage of the natural environment with effects on the quality of life, mainly caused by water, atmosphere and soil pollution, over-exploitation of the resources and their poor management and valorization.
The water is an essential natural resource for today’s society. Therefore, one of the great problems facing humanity is the provision of water resources for urban and rural communities, for various industrial and/or agricultural activities, given that the world’s population is constantly growing, and the industry is constantly evolving. In these conditions, this intensive use of water inevitably leads to its pollution, and by discharging polluted water into groundwater or surface water, to environmental pollution. In this regard, the issue of environmental protection and water resources management has become very pressing and in the permanent attention of the European Union (EU). The European Commission (EC) developed numerous directives [
1,
2] that show the concern of this European forum to prevent the degradation of the environment and the quality of water resources. This set of normative acts has materialized in a program for the protection of Europe’s water resources. A number of quality standards have been imposed with clear objectives for the protection of human health and the environment, including surface water, domestic water, fishery, shellfish (waters for growing various species of mollusks), groundwater and drinking water. One of these legislative directives imposed certain standards relating to the discharge of substances into surface waters. Other directives refer to the urban wastewater treatment (UWWT), to the prevention of pollution in the industrial and agricultural environment, to the water management in the natural hydrographic basins, etc.
Given the importance of the wastewater treatment field, research in this field has evolved in two directions:
Thus, it can be stated that the wastewater treatment field has become an area addressed by numerous interdisciplinary research teams consisting of specialists in microbiology and biochemistry and last but not least, in automation and computer science. Given that these processes are nonlinear, very complex and strongly affected by uncertainties (parameter and model uncertainties), these processes have become a real challenge for specialists in modeling, identification and control in order to develop advanced methods for increasing the efficiency of the wastewater treatment processes.
Among the most well-known mathematical models reported by the literature and accepted by the international scientific community can be mentioned ASM models (Activated Sludge Models). These were developed within International Association on Water Quality (IAWQ), which became later International Water Association (IWA), by a working group led by Prof. Henze, which aimed at developing and applying mathematical models of biological treatment processes in the design and operation of treatment plants. The first activated sludge treatment model (ASM1) [
9] describes the biological oxidation of carbon, the nitrification and denitrification and is, therefore, used to model the removal of carbon and nitrogen in the activated sludge treatment systems. It considers four processes: (1) the growth of autotrophic and heterotrophic bacteria, (2) their degradation, (3) the hydrolysis of the organic particles and (4) ammonification of soluble organic nitrogen. The reaction rate of each process is expressed as a series of Monod-type smooth switching functions, corresponding to certain conditions (for example aerobic, anoxic, anaerobic processes). The ASM2 model [
10] is an extension of ASM1 and includes the biological and chemical removal of phosphorus, in addition to the removal of carbon and nitrogen from the ASM1 model. Subsequently, the ASM2 model was extended to the ASM2d and ASM3 models [
11]. The ADM1 model (Anaerobic Digestion Model) should also be mentioned [
12,
13]. It includes the disintegration from homogeneous particulates to carbohydrates, proteins and lipids; extracellular hydrolysis of these particulate substrates to sugars, amino acids and long chain fatty acids (LCFA), respectively; acidogenesis from sugars and amino acids to volatile fatty acids (VFAs) and hydrogen; acetogenesis of LCFA and VFAs to acetate; and separate methanogenesis steps from acetate and hydrogen/CO
2. In addition to the ASM models, simplified models (Nejjari model) [
14] with four state variables or of black-box type (neural networks [
15]) for designing and testing control structures have been developed. Furthermore, the benchmark models (BSM1, BSM1—LT, BSM2) [
16,
17,
18] were developed within IWA. BSM1 is dedicated to the biological removal of nitrogen and organic matter from urban wastewater. BSM1-LT (Long Term) allowed for the evaluation of the control strategies for longer periods due to the proposed influent profile from 7 to 364 days. Finally, BSM2 represents a significant development of the BSM1 model at the level of the treatment plant, taking into account both the water line and the sludge line. In the field of wastewater treatment process control, the specialists have approached various control algorithms ranging from conventional control structures (PI, PID) to advanced ones (robust [
19,
20,
21,
22], predictive [
23,
24], adaptive [
25], sliding-mode, optimal [
26,
27] etc.) and artificial—intelligence—based ones (fuzzy, neural, expert systems) [
28,
29,
30,
31,
32]. The purpose of these control structures was to increase the efficiency of wastewater treatment processes. Thus, in [
32] the authors propose the control of the dissolved oxygen concentration by fuzzy techniques. The output of the DO fuzzy controller (the airflow) controls the aeration system through its inverse model. In [
24] a predictive control structure of DMC (Dynamic Matrix Control) type for controlling the nitrogen concentrations at the end of the biological treatment is proposed. What also has to be mentioned, is the use of hierarchical structures to control wastewater treatment processes. Thus, in [
33] a control cascade nonlinear adaptive control structure extended by the anti-windup filter is proposed. An IMC (Internal Model Control) algorithm is used in the internal control loop and a DMRAC (Direct Model References Adaptive Control) structure in the external loop. In [
34] the authors propose a hierarchical control structure for a Sequencing Batch Reactor (SBR) in a biological Wastewater Treatment Plant. On the upper level is used an optimizer that provides the optimal operating parameters of the SBR reactor (sequence and durations of individual phases), the optimal desired trajectory of dissolved oxygen concentration and the optimal parameters of the adaptive controller. The second control level contains an adaptive controller and the low level an IMC structure. For optimization, the authors used Artificial Bee Colony and Direct Search Algorithm algorithms. A similar approach can be found in [
35]. Another hierarchical control structure is proposed in [
36]. At the low level an IMC control structure is also used and at the second level, dissolved oxygen concentration is controlled. On the upper control level there is a supervisor with the following functions: management of reactor work cycle, determines the phase length, controls sludge age, calculates setpoint of dissolved oxygen and adapts parameters of the lower control layer.
The paper deals with the optimal-setpoint-based control of a wastewater treatment plant. The wastewater treatment efficiency refers to the removal of organic substances and nitrogen and its components, not phosphorus. This control strategy consists of determining an optimum operating point in relation to a performance criterion that takes into account the quality of effluent, the cost of the wastewater treatment and possible exceeding (expressed as a percentage) of the concentration of pollutants beyond admissible limits. At the same time, the control algorithm must take into account the membership to the operating regime (DRY, RAIN and STORM) by means of a fuzzification block.
The paper has the following structure: the second section presents the structure of the wastewater treatment plant together with the automation equipment, description of the influent used in simulations, definition of the performance criterion for the optimization and the description of the control method; the third section presents and analyzes the results obtained in the paper and the last section is dedicated to the conclusions.