Improving total nitrogen removal using a neural network ammonia-based aeration control in activated sludge process

Wastewater treatment plant (WWTP) is a topic of pivotal importance in providing a sanitary water which is a vital resource for everyone. Protecting water sources and safeguarding the ecosystem is a long-standing challenge for the WWTP industry. WWTP are facing more stringent effluent standards which were formed for a safer ecosystem (Åmand and Carlsson, 2013; Han and Qiao, 2014; Olsson et al., 2014). The WWTP industry must come up with a solution that abide the stringent effluent requirements and is also economical.

Studies have shown that the energy consumption in the biological system such as activated sludge process, biological trickling filters, and membrane bioreactors can be curbed through good control of the aeration system. The issue of energy consumption has been investigated by various researchers and the findings suggest that the aeration section which is needed in the WWTP to detract nitrogen and natural or inorganic carbon in the biological process, contributes to 50–90% of the overall energy requirement of the WWTP (Åmand et al., 2013; Cristea et al., 2011; Ghoneim et al., 2016; Vrečko and Hvala, 2013).

In the last decade, there have been various studies investigating the effectiveness of various controller design utilizing dissolved oxygen (DO) control in lowering the aeration cost. This control configuration is the highlight during that time due to the availability of DO sensor probe that can continuously measure the DO concentration in the tank. The fundamental of using the DO sensor probe is to control the DO supply according to the oxygen demand of the microorganism in the tank. However, this solution has a weakness due to the difficulty in getting the exact value of the actual oxygen demand by the microorganism at a specific time; thus, most of the proposed DO control strategies implemented an elevated DO set point to avoid nitrification failure (Arnell, 2016; Ellis et al., 2009; Medinilla et al., 2020; Uprety et al., 2015). The DO control strategy has been extensively studied and many viable solutions have been developed and proposed, for example model predictive control (MPC) (Cristea et al., 2011; Han et al., 2012; Holenda et al., 2008; Martín et al., 2012; Yang et al., 2013), PID (Du et al., 2018; Husin et al., 2020b; Samsudin et al., 2013), fuzzy and neural network (NN) control (Han et al., 2020).

However, even with the DO control strategy, the aeration cost issues persist as DO control requires aerators and turbines which are operated by electrically powered motors that add extra cost to the system. This calls for a paradigm shift in the choice of methodology to solve the problems of energy consumption and cost of aeration control. This issue was explored in the publication by Åmand and Carlsson (2014) which has suggested that the aeration process can be regulated either using the aeration concentration control or tweaking the DO set point level corresponding to the ammonium (S_NH) concentration in the effluent (Åmand and Carlsson, 2014). During the last ten years, the ion selective electrodes (ISEs) S_NH sensor probe has become available for online process. This is a developing technology and has led to the introduction of ABAC.

ABAC is a control strategy which uses S_NH as response variable in addition to or in place of DO. ABAC has been introduced to overcome some of the inherent limitations of DO control strategy and it is used mainly for two reasons, which are to restrict aeration and to shrink effluent S_NH peaks. Several techniques have been recently proposed regarding ABAC, ranging from a conventional PI-ABAC control (Åmand and Carlsson, 2014; Husin et al., 2020a, 2021a; Várhelyi et al., 2018; Uprety et al., 2015), to advanced MPC ABAC (Santin et al., 2015a, b, 2016). Some of the recent proposals are summarized in Table 1.

According to the literature, most pilot or real plants use the PI control in their ABAC designs. The PI controllers utilized are configured in a decentralized configuration. This configuration is favorable because there is no need to deal with the coupling problem in a multi-input multi-output (MIMO) system. A PI controller, on the other hand, is known for being susceptible to disruptions and/or changes in the condition of the operation. Although advanced control schemes such as MPC have been shown to yield better results than PI controllers, MPC is known to be computationally complex and difficult to apply in a real plant. All the studies in the literature indicated that the MPC is implemented in simulation platform only, e.g., BSM1 and BSM2. Another observation into the recent research trend is the emphasis on aeration energy cost problem but less toward the pragmatic benefits brought by ABAC control strategy on effluent quality which has not been extensively explored by researchers.

Considering the advantages and disadvantages levied by these publications, an alternative control strategy that is more streamlined with lower complexity is desirable especially if the aim is to apply the controller in the real or pilot plant. Prior work in this area has been limited to the use of traditional control methods such as PI/PID control. Traditional approaches, such as PI/PID, have various drawbacks, including performance loss when applied to a highly nonlinear process (Aguilar-López et al., 2016) and the inability to meet the demand for high performance control as operating conditions vary (Samsudin et al., 2014). In light of these drawbacks, this research proposes a NN ABAC. The suggested NN ABAC’s main advantages are its simplicity and ability to effectively decouple. Interestingly, to the author’s knowledge and based on the literature research conducted, there have been no previous publications in the literature on the deployment of NN ABAC control to date. Unlike previous systems that relied on cascade configuration, this concept combines many cascaded controllers into a single NN ABAC controller. As a result, the objective of this study is to develop a NN ABAC controller which could be used in the BSM1 to improve the effluent quality especially the N_tot and S_NH concentration, which are considered as the two most important effluent pollutants of the activated sludge system.

The paper is divided into five sections: the first is an introduction, which includes some earlier and related publications. The simulation model is introduced in the second section. In addition to the typical PI ABAC as a comparison, the third section describes the design and implementation of the proposed NN ABAC. The fourth section summarizes the simulation findings and assesses the proposed NN ABAC’s performance, while the fifth section wraps up the work with some recommendations.

The proposed NN ABAC control is implemented using the MATLAB™/Simulink™ software, and the assessment for this study are established on BSM1. BSM1 is a benchmark simulation plant that consists of a model, a control system, a benchmarking procedure, and evaluation processes. Figure 1 illustrates the BSM1, which is based on the activated sludge model no. 1 (ASM1) and consists of five reactors and a clarifier. Many studies in this field have used this plant as a standard benchmark of comparison.

Figure 1:

The simulation of BSM1 arrangement begins with initialization using 150 days of stabilization in a closed-loop state. Then, it follows by simulation using the dry weather file, and lastly, it progresses with weather files to be validated. The system turns out to be stable if the steady state is reached.

Various control procedures are measured corresponding to a guideline defines for the plant performance, which entails the Effluent Quality Index (EQI) and the Overall Cost Index (OCI) to evaluate the operating cost. The evaluation also includes the computation of the operating time that the intensity of the effluent is exceeding the limit, as shown in Table 2.

The results obtained from the proposed NN ABAC control are compared with the results from PI ABAC, and benchmark PI to ensure its validity. The study focuses on improving effluent quality while maintaining and/or reducing the aeration costs. There are several methods to analyze the data outline in the BSM1. For this study, the NN ABAC control configuration is evaluated using the second level of performance assessment. In this level, the performances are evaluated using three separate categories.

The first category is the average effluent concentration obtained after a 7-day evaluation utilizing dry/rain/storm weather. The result obtained is compared to benchmark PI and PI ABAC which highlighting five key process variables (S_NH, TSS, N_tot, CODt, and BOD5). The maximum value for each effluent is set based on the criteria described in Alex et al. (2008) and the findings are presented in Table 6. Overall, the proposed method marginally outperforms the other methods in TSS and N_tot effluents in all weather. However, the proposed method underperforms in S_NH effluent in all weather conditions, CODt and BOD5 in dry and storm weather. Increases in average S_NH are expected and it may be caused by insufficient DO level supply to the microorganism in the sludge. However, this result alone is not enough to evaluate the performance of the NN ABAC control configuration.

The number of times the effluent criteria were not exceeded over the latest 7 days simulation is the second category in the performance evaluation provided by the BSM1. The observation is carried out for N_tot and S_NH in all three weathers, as well as T_SS during storms, and the results are shown in Table 7. This is the study’s focus, the N_tot and S_NH violations must be kept to a minimum.

The NN ABAC control configuration has demonstrated a marked improvement in the number of effluent violations for N_tot and S_NH in all-weather except once in dry weather for S_NH violation where the result is inferior to PI ABAC by 0.91%. The proposed control configuration exhibits excellent performance in all cases when compared to benchmark PI, as N_tot violation is decreased by 34.99% while S_NH is decreased by 0.89% (dry weather), N_tot violation is decreased by 48.68% while S_NH is decreased by 15.70% (rain weather), and finally, in storm weather, N_tot violation is decreased by 30.81% while S_NH is decreased by 4.52%. The results showed that the proposed controller enhanced the two key effluent performance measures.

To fully understand the results, the performances of N_tot and S_NH over the last 7 days simulation using all-weather file is shown in Figures 9–11. The red dotted line represents the PI ABAC, the black line represents the PI, the blue line represents the NN ABAC. It can be observed that both PI ABAC and NN ABAC show a reduction of N_tot, however, utilizing the NN ABAC control configuration, a further reduction can be achieved. The most captivating finding is that the number of violations is reduced by 28.57%, which is from 7 to 5 violations in dry and storm weather, and in rain weather is reduced from 5 to 3 violations which is about a 40% reduction.

Figure 9:

Figure 10:

Figure 11:

It was found that only a small decrease of S_NH peaks is attained using the NN ABAC control in the BSM1. The improvement was not as obvious as compared to N_tot violation. This finding is expected since there will be a trade-off between one and another. It is difficult to obtain a good result for both S_NH and N_tot without additional control strategies to supply the external carbon flow rate (q_EC) and internal recirculation flow rate (Q_rin).

It is well known that the contaminants S_NH and N_tot are the pollutants that ought to be held below the permitted limits. The suggested NN ABAC will not be able to keep N_tot below the limit, implying that no violation would occur. A potential solution to this difficult problem could involve injecting the q_EC in the first tank which can facilitate in reducing the N_tot peak. This may be explained by the increase of the anoxic growth of X_BH once carbon dosage is added up to the tank. In general, the S_NH violation can be tackled well if the Q_rin is accurately controlled, which perfects the nitrification process of the bacteria in the sludge.

The last category in the performance assessment of the BSM1 is the evaluation of the effluent, aeration, and cost. The results are divided into average EQI, aeration energy cost index (AECI), and the total OCI as per Table 8. As can be seen from the results, the PI ABAC strategy produces the best EQI, while the proposed NN ABAC approach performs just slightly below it, with a deficit of 0.29–0.67%. However, the PI ABAC excess comes at a significant price, as evidenced by the AECI value, which is the highest among the other controllers. The proposed controller, on the other hand, has much lower AECI values, resulting in a reduction of 26.04%. Furthermore, the suggested NN ABAC outperforms the PI ABAC in terms of total OCI reduction by up to 5.15%. In comparison to the PI controller, the NN ABAC consistently outperforms the PI controller across all assessments.

Table 9 shows the comparison results in terms of AECI, EQI, OCI and percentage of time over limits of two highlighted effluents, S_NH and N_tot with other similar studies. The suggested controller has the lowest AECI and total OCI, as stated in the table. When compared to other similar research, the AECI achieved by the proposed controller is lowered by 22%, which is a significant achievement. Aside from that, the proposed controller has the lowest percentage of N_tot breaches. The S_NH violation is not the lowest, but it is not the worst.