Design of Control Loop Pairing in a UCT Bioreactor System
Hisbullah1, Azwar2*, Fauzi3, Anwar Thaib4, Adisalamun5, Mukhlishien6, R. Nasrullah7, Abubakar8, M.F. Zanil9 and J.M. Ali10
1,2,3,4,5,6,7,8Chemical Engineering Department, University of Syiah Kuala, 23111 Banda Aceh, Indonesia
9Chemical and Petroleum Engineering, Faculty of Engineering & Built Environment, UCSI University, 56000 Kuala Lumpur, Malaysia
10Chemical and Process Engineering Department, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
*Koresponden email: [email protected]
Diterima: 18 November 2021 Disetujui: 22 Desember 2021
Abstract
Control of waste water treatment plants (WWTP) has its own complexity, mainly as a result of the nature of the process involving different types of living organisms whose behavior is different from one type to another. This situation is compounded by variations in the characteristics of the wastewater entering the plant. This is essentially the main challenge for control of wastewater treatment plants. In this work, the design of a control system for a WWTP was studied. Multi-loop control configurations were implemented.
The controlled variables are concentration of (Chemical oxygen demand), COD, ammonium, nitrate, and phosphate. Five manipulated variables are available, namely, two internal recycle flows, additional substrate flow, sludge retention time (SRT), and return sludge flow. The disturbance variable accounted for in this study was changes in influent flow rate. The loop pairing structures were first selected based on Relative Gain Array (RGA) analysis, and after scrutinizing the dynamic performance, the modified structure developed through process understanding is suggested.
Keywords: WWTP control, organic matter, control loop pairing, RGA analysis, UCT bioreactor
Abstrak
Pengendalian Instalasi Pengolahan Air Limbah (IPAL) memiliki kompleksitas tersendiri, terutama sebagai akibat dari sifat proses yang melibatkan berbagai jenis organisme hidup yang perilakunya berbeda antara satu jenis dengan jenis lainnya. Situasi ini diperparah dengan variasi karakteristik air limbah yang masuk ke pabrik. Ini pada dasarnya adalah tantangan utama untuk pengendalian instalasi pengolahan air limbah.
Dalam pekerjaan ini, desain sistem kontrol untuk IPAL dipelajari. Konfigurasi kontrol multi-loop diimplementasikan. Variabel yang dikontrol adalah konsentrasi (Chemical oxygen demand) COD, Amonium, Nitrat, dan Fosfat. Lima variabel yang dimanipulasi yaitu, dua aliran daur ulang internal, aliran substrat tambahan, waktu retensi lumpur (SRT), dan aliran lumpur kembali. Variabel gangguan yang diperhitungkan dalam penelitian ini adalah perubahan laju aliran influen. Struktur pasangan loop pertama kali dipilih berdasarkan analisis Relative Gain Array (RGA) dan setelah meneliti kinerja dinamis, disarankan struktur yang dimodifikasi yang dikembangkan melalui pemahaman proses.
Kata Kunci: Kontrol IPAL, bahan organik, pasangan loop kontrol, analisis RGA, bioreaktor UCT
1. Introduction
Water is a compound necessary for survival and is the most important substance in life after air. The demand for clean water resources in modern society continues to increase and grow rapidly, especially for the purposes of civil and industrial activities [1]. The first water quality crisis occurred after the industrial revolution in the early twentieth century. This was due to the absence of an appropriate wastewater management strategy [2-3]. Currently, wastewater treatment and management techniques include redesign of process structures or development of advanced process control strategies with the aim of concentrating various pollutant compounds, both organic and inorganic, such as carbon (C), nitrogen (N), chemical oxygen demand (COD), biochemical oxygen demand (BOD), and phosphorus (P) that can be treated at the lowest possible cost [4-6]. Despite the development of several control methods and the creation of new control systems, many WWTPs continue to run in a traditional manner due to a lack of thorough understanding of modeling, optimization, and planning of appropriate control strategies [7].
The success of organic material (referred to as Chemical Oxygen Demand, COD), nitrogen (N), and phosphorus (P) removal from municipal wastewater in a wastewater treatment plant (WWTP) is associated
strongly with the reliability of the process control system implemented [9-11]. Control of WWTP has its own complexity. This is mainly as a result of the nature of the process involving different types of living organisms whose behavior is different from one type to another. This situation is compounded by variations in the characteristics of the wastewater entering the plant, which is hard to compare with other chemical or biochemical processes [12-15]. This is essentially the main challenge for control of wastewater treatment plants. The difficult part encountered when designing the control system for WWTP is the fact that the process exhibits an interacting nature among variables [16-17]. The decision on selecting the appropriate control strategy is, therefore, very crucial. Two approaches may be adopted, i.e., multivariable control and multi-loop control [18-20].
There has been quite a lot of work dedicated to multivariable control design for wastewater treatment bioreactor systems. Some of them were reported by Wahab et al., [21], Qiao et al., [22], Emil et al., [23], Wei and Jun-Fei [24], Deepnarain et al., [25] and Tahseen et al., [26]. It may be that multivariable control should be implemented because there are multiple control objectives and multiple manipulated variables.
In practice, the implementation of a multivariable control system is, however, difficult to realize, especially when the complexity of the control problem is beyond the simple. For example, it suffers from the cost involved in defining the control problem and setting up a detailed dynamic model. Instead, a multi-loop control system is often used [25; 27]. One reason for this is that it is less sensitive to model uncertainty since it usually does not use an explicit model. The critical issue arising when considering a multi-loop strategy is the selection of the proper pairing system. The combined COD, N, and P removal process is characterized by a highly interacting system. The wide range of process disturbances are really horrible.
Selecting an improper pairing can cause the effluent quality to be even worse than that resulting from an uncontrolled plant [28-29].
There are several analysis methods that can be used to determine the favorable control pairing, e.g., Relative Gain Array (RGA) [30], Relative Disturbance Gain (RDG) [31], and the Gap Metric [32].
Probably, RGA analysis is the simplest method among them since it can be performed based on the process steady state information. It is a well-known and accepted method for the investigation of interactions in MIMO systems [32]. Nevertheless, RGA analysis does not always suggest the best pairing system, but it does suggest the pairing that ensures stability. The combination of RGA analysis results and the designer's prior knowledge of process dynamics is frequently useful in obtaining the appropriate pairing system. This report presents a study on designing a control system for a municipal WWTP bioreactor system performing simultaneous COD, N, and P removal. Emphasis is put on the selection of a multi-loop pairing structure.
The pairing system was chosen using relative gain array analysis (RGA) in conjunction with knowledge of the process dynamics [30].
2. Process Description
Essentially, there are three important components considered pollutants in municipal wastewater, namely, organic material (termed as COD), nitrogen (N), and phosphorus (P). In the effluent, nitrogen may appear in the form of ammonium and nitrate compounds, while phosphorus is in phosphate compounds.
The removal mechanisms of COD, N, and P from wastewater in the bioreactor can be briefly described as follows.
1. COD is removed from the wastewater as the organic material is utilized as substrate (food) by heterotrophic bacteria. The ultimate metabolic product of this utilization is carbon dioxide, leaving the system as gas. This can happen in bioreactors with aerobic (oxygen as the oxidative agent) and/or anoxic (nitrate as the oxidative agent while oxygen is absent) environments.
2. Ammonium is removed from wastewater as it is utilized (after converted into ammonia) as a substrate by autotrophic bacteria. The metabolic product of this utilization is nitrate. Autotrophic bacteria are active in bioreactors with an aerobic environment.
3. Nitrate is removed from wastewater as it serves as an oxidative agent in bioreactors with an anoxic environment. Here, nitrate is converted to nitrogen gas, leaving the system as a gas.
4. Phosphorus is removed from wastewater as phosphate is utilized by a type of bacteria where phosphate is converted to polyphosphate, which is stored in the bacteria’s internal cells. This type of bacteria is called a Phosphorus Accumulating Organism (PAO). Phosphorus leaves the system as these bacteria leave the system, i.e., through the waste sludge stream. The large amount of phosphorus storage is only possible if the PAOs undergo an anaerobic-aerobic environment cycle. In bioreactors with an anaerobic environment, the organisms take organic material and store it as polymer polyhydroxyalkanoate (PHA) inside their cells. At the same time, the polyphosphate inside the cells is degraded to provide energy for the organisms, and phosphate is released. At the same time, PHA is used by the organisms for their
growth, and at the same time, phosphate is taken to recover the level of polyphosphate in their cells [4- 7].
Development of WWTP Bioreactor System Modeling
The UCT (University of Cape Town) bioreactor system is considered in this study [8]. The ASM model has been widely used in various applications, especially for modeling WWTP and activated sludge.
ASM1 has been used to model biological processes in wastewater treatment to reduce COD and nitrogen.
For biological phosphorus elimination, the ASM2 model was employed. In WWTPs, ASM2 is also used to predict the growth of anoxic organisms and anoxic polyphosphates. ASM3 and ASM2d in WWTP and activated sludge have been used to predict sludge production and oxygen consumption by microorganisms, biological phosphorus removal, nitrification and denitrification [33-34].
Table 1. Process condition at base case Components Influent
Stream g/m3
Add.
Substrate Stream
g/m3
Anaerobic g/m3
Anoxic g/m3
Aerobic g/m3
Effluent Stream
g/m3
Waste/Return Sludge stream
g/m3
Sf 41.70 0 1.84 0.70 0.48 0.48 0.47
Sa 27.80 10000 24.28 0.49 0.03 0.03 0.03
Snh 40.3 0 23.48 12.86 1.58 1.58 1.58
Sno 0 0 0.02 1.52 12.34 12.34 12.34
Spo 9.01 0 29.72 18.36 0.60 0.60 0.60
Si 30 0 29.88 29.88 29.88 29.88 29.88
Sn2 0 0 0 30.21 0 0 0
Xi 51.20 0 2124.8 3165.1 3168.4 0 6166.4
Xs 202.32 0 157.93 145.34 108.26 0 214.11
Xh 28.17 0 1681.0 2528.0 2520.5 0 4985.1
Xpao 0 0 579.47 874.86 704.47 0 1393.3
Xpp 0 0 214.28 343.21 354.36 0 700.86
Xpha 0 0 72.66 60.47 21.85 0 43.22
Xa 0 0 102.75 154.95 158.50 0 313.48
Xtss 215.49 0 4574.5 6829.6 6622.2 0 13098
COD = Sf+Sa+Si
99.5 10000 55.68 31.08 30.37 30.37 30.7
Influent flow rate, Qs = 18446.3 m3/day Return sludge flow rate, Qr = 18446.3 m3/day
Internal recycle (Anoxic to Anaerobic tank) flow rate, Qir1 = 36892.6 m3/day Internal recycle (Aerobic to Anoxic tank) flow rate, Qir2 = 36892.6 m3/day Additional substrate flow rate, Qad = 75 m3/day
SRT = 10 days
The UCT bioreactor system, as shown in Figure 1, was modeled by deriving material balance around the anaerobic, anoxic, aerobic, and clarifier tanks. The kinetic models used to express the biochemical reactions were based on the Activated Sludge Model No. 2d (ASM2d) (Henze et al., 2000). Assumptions made are as follows:
1. The dynamics of oxygen concentration were neglected. Its concentration in the aerobic tank was set to a large number so that the kinetic term containing it has a value of unity.In the anaerobic and anoxic tanks, its concentration was set to zero.
2. The clarifier was modeled by not taking into account the process dynamics; all solid material is settled, the effluent stream is free of solids, and no reaction occurs.
In the base case, the influent wastewater has characteristics (concentration of components and flow rates) as reported by Klaus and Bott (2020). The additional substrate stream was assumed to contain acetate only (Sa) with concentration 10000 g COD/ m3. The volumes of anaerobic, anoxic, and aerobic tanks were assumed to be 1250, 2000, and 2000 m3, respectively. Based on the these data the process was simulated until the steady state was achieved with concentration of COD, Snh, Sno, and Spo in effluent being reasonable as the allowable quality of treated wastewater. The variables considered during obtaining base case process condition were the manipulate variables, i.e. Qir1, Qir2, Qad, SRT, and Qr. After as series of
simulation on different combination of the manipulated variables’ value, it was obtained at steady state that the effluent contains concentrations of Snh, Sno, Spo, and COD are 1.58 g N/m3, 12.34 g N/m3, 0.60 g P/m3, and COD 30.7 g COD/m3, respectively. These values were assumed to be satisfactory for the allowable effluent quality. The values of the manipulated variables and the other model’s state variables at this condition are given in Table 1.
Development of WWTP Bioreactor Control System
In this study, the UCT bioreactor system was considered as the plant to be controlled (Figure 1). The purpose of the control system to be designed was to maintain the effluent quality at its set point in spite of the presence of disturbances. The control strategy implemented was multi-loop control. The controlled variables are COD, ammonium (Snh), nitrate (Sno), and phosphate (Spo). Five manipulated variables are available, namely, internal recycle flow 1 (Qir1), internal recycle flow 2 (Qir2), additional substrate flow (Qad), sludge retention time (SRT), and return sludge flow (Qr). It is important to note that SRT is a parameter which is associated with waste sludge flow rate (Qw). Manipulating SRT means manipulating Qw. The considered disturbances were changes in influent concentration of COD, ammonium (Snh), and phosphate (Spo) and influent flow rate [35-37].
Control Pairing Selection
Two control pairing structures are studied. The first structure (called Structure I) is selected based on RGA analysis (not shown here due to limited space). The second structure (called Structure II) is designed after investigating the performance of Structure I. Again, due to limited space, the performance of the two structures is investigated only in the case of a change in influent flow rate as an external disturbance. The control system structure is shown in Figure 1. Here, Qr remains constant during the operation. The pairing system is as follows: Spo is paired with Qad, Sno with Qir2, Snh with SRT, and COD with Qir1.
Fig. 1. The UCT bioreactor control system: Structure Performance Investigation
A case with a 35% step increases in influent flow rate is considered. As the controller, the proportional-integral (PI) algorithm is used. The trial-and-error tuning method is applied. The considered best tuning parameters of the controllers of the original design are listed in Table 2.
Table 2. Tuning parameters of the PI controllers for structure I.
Loops Control Parameters
Kc TI
COD – Qir1 -5000 10
Snh – SRT -20 0.1
Sno – Qir2 -10000 10
Spo – Qad -100 1
Effluent Qir2
Qir1
Influent
Anaero bic tank
Anoxic tank
Aerobic
tank Clarifier
Qw Qad Qr
Sno Spo
Snh COD
The constraints imposed on the manipulated variables were assumed to be as follows:
1. Qad has a range of 40% to 400% of its base case value.
2. Qir1 has a range of 35% to 150% of its base case value.
3. Qir2 has a range of 50% to 200% of its base case value.
4. SRT has a range of 80% to 200% of its base case value.
3. Results and Discussion Structure I
The dynamics of the uncontrolled and controlled plants in response to the step increase in influent flow rate are shown in Figure 2 and Figure 3. It can be seen that, by comparing the uncontrolled and controlled dynamics, the control system did not perform well in rejecting this disturbance. During the short period after the disturbance is introduced, a sharp increase in the concentration of Spo (phosphate) and SNH (ammonium) in effluent occurs. The Spo controller responds to this by increasing Qad, leading to an increase in COD level in the anoxic tank while the SNH controller increases SRT. The SNH level slowly recovers, and the SRT remains saturated for an extended period of time. This high SRT value led to a deleterious effect on Spo concentration (less Spo is removed as less sludge is wasted), i.e., an even much higher Spo concentration resulted as compared to the fluctuation of the uncontrolled result.
Fig. 2. The uncontrolled (dotted line) and controlled (solid line) process responses to a 35% step increase in influent flow rate for controlled variables.
Due to the abundance of COD in the anoxic tank, the removal of Sno is favored during this period.
But, later SN concentrations fluctuate severely as a result of the fluctuation in COD. It seems that the dynamics of Qad as a result of the Spo control action has a stronger effect on the COD dynamics than that of Qir1 (as a COD manipulated variable) does.
Fig. 3. The uncontrolled (dotted line) and controlled (solid line) process responses to a 35% step increase in influent flow rate for manipulated variables.
Structure II
From the above analysis, it is identified that the COD control loop is the key factor to be considered to have the simultaneous phosphate and nitrogen removal improved. The use of Qir1 as the manipulated variable for the COD control loop is found unfavorable.
Fig. 4. The modified bioreactor control system design
Also, the use of Qad as the manipulated variable for the Spo control loop is unfavorable for Sno removal. A modification to the control pairing system is, therefore, made. The manipulated variables for Spo and COD control loops are switched, i.e., Qir1 serves as the manipulated variable for Spo control while Qad serves as the manipulated variable for COD control. The sketch of the control system Structure II is shown in Figure 3. The parameters for the two PI controllers are retuned and the result is as given in Table 3.
Qr Qir2
Qir1
Influent
Anaero bic tank
Anoxic tank
Aerobic
tank Clarifier
Qad Qw
Sno Spo
Snh COD
Table 3. The tuning parameters for PI controllers for Structure II
Loops Control Parameters
Kc TI
COD – Qad 200 0.1
Snh – SRT -20 0.1
Sno – Qir2 -10000 10
Spo – Qir1 -1000 0.1
The control result of this structure for the case of 35% step increase in influent flow rate is shown in Figure 5 and Figure 6. Unlike the result of Structure, I, this structure provides good control of COD and hence, fluctuation in Sno concentration in effluent can be reduced. For long time period, Snh concentration can be maintained close to its set point. In spite of the advantages, control performance for Spo removal is not satisfactory. The control system could not bring Spo concentration back to its set point. This is caused by the constraint of Qir1.
Fig. 5. The uncontrolled (dotted line) and controlled (solid line) process responses with the modified pairing system to a 35% step increase in influent flow rate for controlled variables.
Fig. 6. The uncontrolled (dotted line) and controlled (solid line) process responses with the modified pairing system to 35% step increase in influent flow rate for manipulated variables.
4. Conclusion
The study presented the development of a bioreactor control system design in a wastewater treatment plant. The design is multi-loop in structure. A PI controller is used in each loop. The loop pairing system is, in the first step, determined via RGA analysis (Structure I). The difficulty in tuning the PI controller was encountered when implementing the control pairing system suggested by RGA analysis. This was because the pairing system led to conflict between Spo and Sno removal processes through COD dynamics. This result is not surprising since pairing system design using RGA analysis is merely based on the process steady state information. Process performance improvement can be achieved by modifying the loop pairings (Structure II).
5. Acknowledgments
The authors are grateful to Chemical Engineering Department, Faculty of Engineering at Universitas Syiah Kuala for technical support.
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