**Silvana Revollar 1,\*, Montse Meneses 2, Ramón Vilanova 2, Pastora Vega <sup>1</sup> and Mario Francisco <sup>1</sup>**


Received: 31 December 2019; Accepted: 5 February 2020; Published: 7 February 2020

**Abstract:** In this work a comprehensive analysis of the environmental impact of the operation of a wastewater treatment plant (WWTP) using different control strategies is carried out considering the dynamic evolution of some environmental indicators and average operation costs. The selected strategies are PI (proportional integral) control schemes such as dissolved oxygen control in the aerobic zone (DO control), DO control and nitrates control in the anoxic zone (DO + NO control) and regulation of ammonium control at the end of aerobic zone (Cascade SNHSP) commonly used in WWTPs to maintain the conditions that ensure the desired effluent quality in a variable influent scenario. The main novelty of the work is the integration of potential insights into environmental impact from the analysis of dynamic evolution of environmental indicators at different time scales. The consideration of annual, bimonthly and weekly temporal windows to evaluate performance indicators makes it possible to capture seasonal effects of influent disturbances and control actions on environmental costs of wastewater treatment that are unnoticed in the annual-based performance evaluation. Then, in the case of periodic events, it is possible to find solutions to improve operation by the adjustment of the control variables in specific periods of time along the operation horizon. The analysis of the annual average and dynamic profiles (weekly and bimonthly) of environmental indicators showed that ammonium-based control (Cascade SNHSP) produce the best compromise solution between environmental and operation costs compared with DO control and DO + NO control. An alternative control strategy, named SNHSP var Qcarb var, has been defined considering a sequence of changes on ammonium set-point (SNHSP) and carbon dosage (Qcarb) on different temporal windows. It is compared with DO control considering weekly and bimonthly profiles and annual average values leading to the conclusion that both strategies, Cascade SNHSP and SNHSP var Qcarb var, produce an improvement of dynamic and annual average environmental performance and operation costs, but benefits of Cascade SNHSP strategy are associated with reduction of electricity consumption and emissions to water, while SNHSP var Qcarb var strategy reduces electricity consumption, use of chemicals (reducing external carbon dosage) and operation costs.

**Keywords:** wastewater treatment plants; environmental costs; PID control; dynamic assessment of performance

### **1. Introduction**

Historically, the primary objective for collecting wastewater was sanitation to prevent the spread of waterborne diseases. Nowadays, wastewater treatment continuously evolves as the awareness of emerging environmental problems grows. The knowledge about the influence of human activities on climate change has widened the scope for treatment plants beyond only effluent water quality and cost. Today greenhouse gas emissions, energy efficiency and resource recovery also need to be

considered when evaluating operational strategies by also minimizing the operational costs in order to achieve sustainable treatments. The optimization of the operations of a wastewater treatment plant (WWTP) is not an easy task. The influent load is constantly varying in flow and concentration, is naturally uncontrolled and arrives every hour of the day, all year round. Rainfall events affect wastewater composition because, in combined sewers, these events increase the flow and pollutants stored in sewer sediments and/or deposited on impervious surfaces are washed out [1,2]. A wastewater treatment plant cannot shut down for review and maintenance. Moreover, the construction with sequential unit processes in combination with multiple return feeds create numerous feed-back effects that makes the processes interconnected in an intricate manner. A WWTP should be considered as an integrated process, where primary/secondary clarifiers, activated sludge reactors, anaerobic digesters, thickener/flotation units, dewatering systems, storage tanks are interconnected and need to be operated and controlled not as individual unit operations, but taking into account all the interactions amongst the processes. Models should describe the processes and their interactions in detail considering the ambient conditions. Thereby, the plant-wide effects are captured so that the overall result can be surveyed, analyzed and sub-optimization avoided. In this complex scenario mathematical modelling and simulation provide a solid base for decision support when evaluating WWTP operations.

Researchers and design engineers in wastewater treatment (WWT) are aware of alternative modelling approaches that can be used to evaluate the appropriateness of control strategies to ensure the quality of the treated water with respect to the regulations in the presence of frequent and large disturbances and variable influent characteristics. The control of the activated sludge process (ASP) is crucial for the appropriate operation of WWTPs. ASP is a commonly used biological treatment, especially in large wastewater catchments. In this biological process, the control of aeration is particularly demanding: its optimization is linked to the minimization of the energy used in a plant [3]. Through modelling and simulation studies, not only can the present operations be evaluated but also future scenarios investigated, for example: load forecasts, plant expansions or alternative operational strategies. Some recent works [4,5] have demonstrated how model-based tools can be used in practice to improve the performance of WWTPs. A scenario-based optimization approach that connects effluent quality variables and energy demand and production, by a simulation procedure is proposed in [4] to improve the energy efficiency of an Italian WWTP using the model developed and calibrated in [6]. Potential savings on annual average energy consumption are reported and effluent quality is improved by operational changes, furthermore, the results showed that modifications in design could affect positively the energy and greenhouse gas balance of the plant. In [5], mass balances have been used to evaluate the impact of operation and plant parameters on nitrogen and organic matter removal efficiencies in another Italian WWTP.

An appropriate management of WWTP can produce significant economic and environmental benefits. A holistic assessment procedure that considers the environmental costs of wastewater treatment is necessary to attain a sustainable operation, minimizing energy consumption and greenhouse emissions. Previous works [7–11] address the integration of the analysis of environmental impact on the evaluation of performance of control strategies applied to WWTP. Specifically, annual-based life-cycle assessment (LCA) is used for the evaluation of economic and environmental performance of a WWTP in [8,9], LCA is applied considering annual average inventory. Global performance indicators are proposed in [10,11]. In [10] an integral performance index that quantifies the effect of the main control actions on water quality, operational cost and greenhouse gas emissions is used to measure the global positive effect of control systems on the plant operation. In [11], an overall efficiency index is used as the controlled variable of a holistic optimizing proportional integral (PI)-control strategy that introduce plantwide considerations.

Nevertheless, there are few studies discussing the additional benefit of adding a new dimension related to dynamic analysis within the performance evaluation procedures [12]. Regarding the LCA methodology that is typically used to evaluate environmental impact of production systems, several authors have critic the lack of a temporal dimension, even though inputs and environmental

mechanisms are time varying [12]. Few works can be found that consider the time dependency of indicators of environmental performance of WWTPs. In [12] a dynamic LCA methodology is proposed and a WWTP is used as case study to evaluate the sensitivity of LCA results to temporal parameters. In [11], the evolution of environmental performance indicators is considered in [11] to evaluate the impact of control strategies. In [13], a dynamic model of activated sludge reactors working under an intermittent aeration regime is developed to evaluate the link between aeration and effluent quality, the analysis of airflow rate influence on performance allow to increase process efficiency, producing a reduction of 14.5% on power consumption. The selection of a time horizon is equivalent to giving a weight to time and is one of the most critical parts of the carbon accounting processes [14,15].

The Benchmark Simulation Model No. 2 (BSM2) is a standard simulation model developed as a reference scenario to implement and evaluate control strategies [7–11,16–19]. The BSM2 represents the water line and the sludge line of a municipal WWTP considering a dynamic influent that contains everything from short-term diurnal variations and weekend effects to long-term variations for temperature and holidays periods [14,17,18]. The BSM2 platform is selected in this work to represent a municipal WWTP, in order to demonstrate the benefit of adding this extra dynamic dimension to the simulation.

In this paper a comprehensive analysis of the environmental impact of control/operational strategies is performed through a dynamic perspective from a WWTP operation. The main novelty of the work is the introduction of potential insights into environmental impact from the analysis of the evolution of environmental indicators considering different time scales: annual, bimonthly and weekly. The consideration of different temporal windows makes possible to capture periodic seasonal effects associated with influent variations and interactions between control actions and environmental costs of wastewater treatment that are unnoticed in the traditional performance evaluation based on the analysis of annual average indicators. Indeed, the analysis of plant behavior in shorter time horizons makes possible to capture dynamic effects that are hidden by the evaluation using annual based indicators of performance.

The main objective is to show the benefits that result from adding dynamic perspective to plant performance evaluation criteria evaluation of control/operational strategies. The analysis makes possible to find solutions to improve operation by the adjustment of the control variables in specific periods of time along the operation horizon. It allows to improve the wastewater treatment in terms of energy efficiency, resources recovery and greenhouse gas emissions, while not compromising effluent quality and still maintaining control of the operational cost.

The remainder of this paper is organized as follows. Descriptions of a reference wastewater treatment plant (BSM2 model), control strategies and environmental performance indicators are presented in Section 2. Results of the evaluation of annual average and dynamic (weekly and bimonthly temporal widows) of environmental costs considering the selected control strategies and the results of the comparison with alternative operation strategy are presented in Section 3, closing with some conclusions.

### **2. Materials and Methods**

### *2.1. Description of the Wastewater Treatment Plant and Control Strategies. Benchmark Simulation Model No. 2 (BSM2) Platform*

The process lines commonly distinguished in a municipal WWTP are water line, where pollution removal is carried out, sludge line and gas line. In this work, the water and sludge lines of a municipal WWTP are represented using the BMS2. This recognized simulation platform describes the plant layout, the simulation model, the influent profile and the evaluation protocol [17–19]. The BSM2 plant comprises primary clarification and activated sludge process units in the water line and anaerobic digestion, thickening and dewatering operations in the sludge line (Figure 1). The plant is designed for an average influent dry weather flow rate of 20,648.36 m3/d and an average biodegradable chemical oxygen demand (COD) in the influent of 592.53 g/m3. Its hydraulic retention time, computed considering average dry weather flow rate and total tank volume of 18,900 m3, is 22 h (total tank volume includes: primary clarifier (900 m3) + biological reactor (12,000 m3) + secondary clarifier (6000 m3)) [18,19].

**Figure 1.** Benchmark Simulation Model No. 2 (BSM2) plant layout.

In the water line, a modification of the benchmark simulation model 1 (BSM1) is used to represent the activated sludge process (ASP) where biological nitrogen and organic matter removal take place by means of nitrification and denitrification processes [16]. In nitrification process, nitrogenated compounds (mostly in the form of ammonium NH4) are sequentially oxidized to nitrite and to nitrate by autotroph bacteria that are strict aerobes, while heterotrophs transform nitrates in nitrogen gas (N2) by denitrification. These biological processes are carried out in a system of 5 bioreactors in series. The first two reactors are anoxic and perfectly mixed to facilitate denitrification, the last three reactors are aerated to promote the nitrification step. Nitrate is recirculated from the aerobic to the anoxic zone (internal recycle flow Qa). A secondary clarifier separates clean water from sludge. The clean effluent (Qe) is discharged and the sludge is partly a wastage flow (Qw) that is fed to the sludge line and partly recycled to the anoxic zone (external recycle flow Qr). The Activated Sludge Model no. 1 (ASM1) [20,21] describes these biological processes and the effect of temperature in the biological kinetics considering eight biological processes and 13 state variables on each reactor. The settler (secondary clarifier) is described using the model of Takács et al. [22].

In the sludge line, a thickener prepares the sludge collected from the primary and secondary clarifiers for the anaerobic digestion. A dewatering unit is used to increase the concentration of the stabilized sludge. As shown in Figure 1, there is a storage tank before recycling the remaining sludge to the water line and the liquids collected in the thickening and dewatering steps are recycled to the primary settler [18]. The digester is modeled using the anaerobic digestion model (ADM1) of [23].

### Significant Operating Variables and Control Strategies

Since the main objective of a BSM2 plant is nitrogen removal, the aim of the control strategies applied to the BSM2 plant is to ensure the appropriate conditions for nitrification/denitrification processes in the ASP. Dissolved oxygen (DO) concentration in the aerobic zone is a determining variable for oxidation mechanisms involved in nitrification process. On the other hand, carbon dosage is a key variable when external carbon source is required in the anoxic zone to provide readily biodegradable substrate to heterotrophs. The sludge age or solids retention time (SRT), which is a measure of the time that sludge (cells, microorganisms) remain in the system, and the food to microorganism ratio (F:M), that represents the balance between the quantity of substrate available and the quantity of microorganisms in the biological reactors, are other important factors for the appropriate course of biological reactions.

The available control handles in the ASP bioreactors are the airflow rate, the internal recirculation (Qa), the sludge recirculation (Qr), the sludge purge flow (Qw) and the external carbon dosage (Qcarb). Then, DO control or ammonium-based supervisory control (regulation of ammonium concentration SNH) in the aerobic basin is performed by manipulation of the airflow rate. Control of nitrates concentration (SNO) in the anoxic zone is carried out by manipulation of internal recirculation flow (Qa) that transport nitrates produced in the aerobic zone to anoxic zone. Moreover, carbon dosage (Qcarb) is a manipulated variable that affects nitrates concentration (SNO) also. In practice, it is usual to maintain constant values of Qa and Qcarb over long periods of time to regulate SNO. Purge flow (Qw) is used to regulate the sludge age (or SRT) and external recirculation (Qr) regulates the F:M ratio. A detailed description can be found in [24,25].

In the sludge line, the loading rate, given in part by ASP purge flow (Qw), and the composition of the input sludge flow affect the characteristics of the biogas and stabilized sludge in the anaerobic digestion. Temperature is another critical variable, that is maintained between 32–35 ◦C using energy from biogas to heat the sludge input flow. Other important operation variables are the solids retention time (more than 20 days) and pH (6.8–7.2).

• Operation strategies: BSM2 default strategy and modifications

The default operation strategy of BSM2 plant includes a DO control scheme in the activated sludge process, distinguished as a DO default control scheme, and open-loop actions to regulate the levels of nitrates in the system, sludge age, F:M ratio control and digester temperature:


The objective of operation strategy is to maintain levels of pollutants in the effluent, such as Nitrogen (Ntot), ammonium (SNH), nitrates (SNO), total chemical oxygen demand (CODt), total suspended solids (TSS) and biological oxygen demand (BOD5), bellow the limits given by effluent quality requirements. The indicators considered in this work are given by BSM2 platform [17–19]: Ntot < 18 gN/m3, SNH < 4 gN/m3, CODt < 100 gCOD/m3.

In this work, two alternatives to the DO control scheme of the default BSM2 strategy described above (Figure 2a) are considered (See Figure 2b,c):



(**c**)

**Figure 2.** Operation strategies applied to BSM2 platform. (**a**) BSM2 Default operation strategy (dissolved oxygen (DO) default), (**b**) BSM2 Default operation strategy with nitrates control scheme (DO + nitrates control (NO)), (**c**) BSM2 Default operation strategy with ammonium-based control scheme (Cascade SNHSP).

The three closed-loop control schemes (DO default, DO + NO control and Cascade SNHSP) use PI controllers of the form: *u*(*t*) = *Kp* · *<sup>y</sup>*(*t*) <sup>−</sup> *ysp* + *Kp Ti <sup>y</sup>*(*t*) <sup>−</sup> *ysp dt* + <sup>1</sup> *Tt* (*y*lim − *y*(*t*))*dt* where *u* is the manipulated variable, *y* is the controlled variable, *ysp* the desired set-point, *y*lim the limit values of the controlled variables, *Kp* is the proportional gain and *Ti* the integral time and *Tt* the anti-windup constant. The tuning parameters of the controllers can be found in [19] for DO controller, [20] for nitrates controller and [10] for ammonium controller.

Table 1 summarizes the BSM2 operation strategy, including the three possible control schemes studied in this paper and the effluent quality objectives.


**Table 1.** BSM2 Default operation strategy with the alternative control schemes.

SO4SP is DO set-point in the 4th reactor, SNO2SP is nitrates set-point in the 2nd reactor, SNHSP is ammonium set-point in the 5th reactor.
