The aim of this paper is to establish and quantify different operational goals and control strategies in autothermal thermophilic aerobic digestion (ATAD). This technology appears as an alternative to conventional sludge digestion systems. During the batch-mode reaction, high temperatures promote sludge stabilization and pasteurization. The digester temperature is usually the only online, robust, measurable variable. The average temperature can be regulated by manipulating both the air injection and the sludge retention time. An improved performance of diverse biochemical variables can be achieved through proper manipulation of these inputs. However, a better quality of treated sludge usually implies major operating costs or a lower production rate. Thus, quality, production and cost indices are defined to quantify the outcomes of the treatment. Based on these, tradeoff control strategies are proposed and illustrated through some examples. This paper's results are relevant to guide plant operators, to design automatic control systems and to compare or evaluate the control performance on ATAD systems.

INTRODUCTION

Autothermal thermophilic aerobic digestion (ATAD) (Lapara & Alleman 1999; Staton et al. 2001) is an advanced sewage sludge treatment with two main goals: a high degree of stabilization (reduction of organic matter that attracts flies, mosquitoes and rodents, and that produces bad odors) and pasteurization (pathogen reduction) of the raw sludge, resulting in Class A biosolids as an end-product (Scisson 2003). Some regulations (European Commission 2000; USEPA 1990, 1993) establish criteria to achieve these goals through simple rules.

In this framework, the ATAD process control is still a challenge for several reasons: it is a batch-mode process versus the well-established control rules for continuous processes; the variability of the influent raw sludge leads to a non-repetitive process that is difficult to model and to control; and the existence of few process variables that can be measured online in a robust way to track the digestion status.

From a benchmark simulation model (BSM) of ATAD, this work analyzes the batch-mode control of the digester by manipulating either the air-flow rate or the sludge retention time. Thus, the main biochemical variables and organic matter indicators are related to temperature evolution during the batch duration. As long as the temperature is the only online, robust, measurable variable in practice, this is used to track not only the pasteurization, but also the stabilization levels. Saving aeration energy or the amount of treated sludge appear as other control goals in addition to improving the sludge quality. Both are related to air-flow and sludge retention time.

In the design of control systems for sludge treatment, control specifications must respond to environmental, industrial, business, social and/or political policies that have to be related to process variables. From this point of view, we are considering three main goals in ATAD management: operating costs, production rate and product quality. These goals can be simultaneously achieved only to some extent. Consequently, tradeoff control strategies are proposed. To evaluate these some performance indices are defined. This paper's results will be relevant to guide plant operators, to design automatic control systems and to compare or evaluate the control performance of ATAD systems.

The paper is organized as follows: the following section includes an in-depth analysis of ATAD process variables; the next section discusses the management goals and defines indices to measure them. Thus, control strategies are presented and some examples are shown in a further section. The final section presents the main conclusions.

ATAD ANALYSIS

Autothermal thermophilic aerobic digestion

ATAD treatment aims for sludge stabilization and pasteurization. The raw sludge is treated in a biological reactor in the presence of oxygen. In general, the reactor is operated in batch mode. Supplying a suitable amount of air reduces the sludge organic matter (stabilization). The digestion of the organic matter generates heat. This heat leads to pasteurization of the sludge during the batch time.

Two main manipulated inputs can be considered to regulate the ATAD reaction: the air-flow rate (Qa) and the solids retention time (SRT). The air-flow rate provides the major controllability (Breider & Drnevich 1981) due to the aerobic condition of the reaction. Owing to the batch-mode operation, the SRT can also be manipulated (Cheng & Zhu 2008) by either changing the batch duration, or the volume of sludge treated per batch.

Conversely, several variables define the process status. The main chemical transformations are: the substrate solubilization of the readily solubilizable substrate (Xr) due to the thermal shock effect; the hydrolysis that represents the solubilization of the slowly biodegradable substrate (Xs) to readily biodegradable substrate (Ss); the aerobic degradation of Ss, carried out by the heterotrophic bacteria (Xbh) using dissolved oxygen (SO2); and the lysis of the bacteria due to the endogenous respiration and cellular death, which produces Xs and particulate inert organic matter (Xi). In particular, Ss, Xs and Xbh are used in this work as the most representative outputs among the biochemical outputs to analyze the organic matter concentration during the reaction. There are also indicators such as the volatile solids (VS) or the biodegradable chemical oxygen demand (bCOD), which collect information on organic matter concentration. They can be computed as 
formula
1
 
formula
2
where γTOD,i defines the amount of oxygen required to oxidize the elements that conform an organic or inorganic compound into its reference compounds (Gujer et al. 1999). However, all these biochemical variables can only be analyzed in the laboratory with a delay of several days, and then used as output-checking variables or for offline analysis.

In fact, there are few variables of the status process that are measurable online. The sludge temperature (Ti) is the most reliable due to sensor robustness. Other additional online measurements are the oxygen reduction potential (Wareham et al. 1994) or the fluorescence of some biological compounds (Kim & Oh 2009).

Subsequently, to attend to both pasteurization and stabilization, a key point in the automatic control of ATAD is to knowing the relation between Ti (the only variable measurable in practice) and the true organic matter reduction. In this sense, Zambrano et al. (2009) described a relation between a bending-point occurrence in the Ti evolution during the batch and the maximum degradation of organic matter. Nájera et al. (2013) proposed an average temperature Tavg that would be computed from Ti records during a batch period. Thus, for a certain composition of the inlet sludge there was an optimum aeration level () that achieved the maximum degradation of organic matter with the least possible air consumption. Moreover, Tavg would reach its maximum and the bending-point in the Ti profile would repeatedly appear at the end of the batch time.

Under this scenario, this paper will study the influence of digester-manipulated inputs, Qa and SRT, in several outputs of the digester's status (biochemical variables and indicators, and temperature). The final purpose is to define how the temperature (Ti or Tavg) can be used for ATAD online control.

ATAD simulation model and experiment setup

For the ATAD analysis, a mathematical model of dynamical equations (Gómez et al. 2007), together with a BSM referred to as AT_BSM (Zambrano et al. 2009) are used. The AT_BSM was implemented in Matlab/Simulink®. The model equations describe the biochemical transformations based on the standard activated sludge models of IWA (Henze et al. 2000) and the physico-chemical transformations associated with the chemical equilibrium and mass transfer between the liquid and gaseous phase of the digester. A total of 22 dynamic variables are included in a nonlinear state space model.

According to the AT_BSM, the influent definition consists of: (i) a stationary composition, given by simulations of the BSM2 evaluated by Vrecko et al. (2006); and (ii) a significant variability of the biodegradable content. Analysis of the raw sludge composition in the BSM2 (Jeppsson et al. 2007) shows that two-thirds of the mixed raw sludge is due to Xs. Therefore, this study will use Xs as the principal variable to quantify the biodegradable organic matter content in the raw sludge.

A 24 h cycle sequence is defined in the batch-mode operation of AT_BSM: 0.5 h for feeding, 23 h for aerated reaction and 0.5 h for emptying. The aeration level Qa remains constant for the whole batch time. For each batch, a different portion of the total reactor volume can be removed, which determines the SRT. As long as the manipulated variables (Qa and SRT) will only be changed between batches, a batch-averaged analysis of the output variables can be carried out.

ATAD simulation experiments

According to the influent composition and the air-flow rate Qa applied, the ATAD performance can be labeled as oxygen-limited (under-aerated) when part of the total organic matter is not digested when the batch time is over, due to the insufficient level of air injected (). Conversely, the batch performance is substrate-limited (over-aerated) when the excess of air () allows the complete digestion of the organic matter but also cools the reactor unnecessarily. Gómez et al. (2007) showed typical profiles of Xs, Ss, SO2 and Ti for the case of substrate-limited and oxygen-limited conditions.

Here, each simulation on AT_BSM takes 50 batch periods. During the simulation time, neither the inlet composition, nor the manipulated variables (Qa and SRT) are modified. Figure 1 shows the results of the fiftieth batch for three different operating conditions: substrate-limited, oxygen-limited and optimal aeration.

Figure 1

Profiles of the fiftieth batch under substrate-limited/oxygen-limited/optimal conditions for (a) Ti and Xs; (b) VS and bCOD; (c) Xs and SO2; (d) Ss and Xbh.

Figure 1

Profiles of the fiftieth batch under substrate-limited/oxygen-limited/optimal conditions for (a) Ti and Xs; (b) VS and bCOD; (c) Xs and SO2; (d) Ss and Xbh.

From Figure 1(a) and 1(b), the biochemical variable Xs and several offline measurable indicators, such as VS and bCOD, show that there is an optimal aeration that gives maximum organic matter reduction. Note that VS and bCOD (see Expressions (1) and (2)) share several terms and consequently their evolution shows a similar tendency along the batch.

Under substrate-limited conditions, the dissolved oxygen SO2 is low at the beginning of a batch and increases when the organic matter has been digested (see Figure 1(c)). However, SO2 does not increase in oxygen-limited or optimal aeration conditions. The variables Xbh and Ss change (see Figure 1(d)) according to the kinetics of the biochemical transformations of the process.

Observe from Figure 1 that batches in oxygen-limited conditions do not reach the maximum organic matter degradation and the stationary operating point gives a lower bacteria population than other conditions. Substrate-limited conditions do not give either the maximum temperature, or the highest bacteria population, or the minimum aeration cost. However, the optimal aeration case achieves maximum degradation and the highest temperatures Ti. Furthermore, it preserves a higher bacteria population and maintains the oxygen concentration on its minimum level. These characteristics not only improve the operating cost and the quality of the sludge, but also the efficiency of the process and the response to disturbances. In summary, a maximum Ti involves the best evolution for the whole set of biochemical variables and organic matter content indicators. A batch-averaged temperature Tavg can be computed from the Ti records in the batch, which will be the main variable to track the status of the process treatment.

 Figure 2 depicts the stationary Tavg versus different values of the manipulated inputs (Qa and SRT) and different inlet conditions (Xs,in). SRT is manipulated by modifying the volume treated during the one-day batch. Tavg values belong to the fiftieth batch of the simulation. Note that there is an optimum pair {, } for each combination {Xs,in, SRT}. Beyond this maximum, an increase of the air-flow cools the digester.

Figure 2

Stationary analysis of Tavg versus Qa for raw sludge with (a) different Xs,in content and fixed SRT and (b) fixed Xs,in content and various SRT.

Figure 2

Stationary analysis of Tavg versus Qa for raw sludge with (a) different Xs,in content and fixed SRT and (b) fixed Xs,in content and various SRT.

The bottom plot in Figure 2(b) represents different ratios between the air-flow and the treated-sludge volume (all under optimal conditions, i.e., achieving the maximum average temperature) versus different SRT. Then, the aeration energy per unit of sludge volume that is necessary for maximum organic matter degradation decreases when the SRT increases. Note that the ratio is reduced by 18% when the SRT increases from 10 to 14 days.

ATAD CONTROL GOALS AND THEIR PERFORMANCE EVALUATION

The ATAD operation may follow diverse management interests. Likewise, the environmental laws are different in every country. We are considering three goals of special relevance in ATAD control, which are closely related to the results discussed in the previous section. To further evaluate the performance or compare different control strategies, several indices are also being defined to quantify each goal fulfillment.

Operational cost

Economic criteria are a priority in both public and private management of plants. The aeration is a relevant factor in aerobic treatment, since it affects both the quality of the effluent and the total operating costs. Thus, the use of Qa is crucial for minimizing the operational costs, with some tradeoffs to consider. Over-aeration increases costs without leading to a significantly better quality of treated sludge; what is more, a cooling effect on the slurry inhibits sludge pasteurization. Under-aeration reduces costs, but also the quality of the treated sludge, since a lower Tavg involves poorer pasteurization and lower organic matter reduction. The use of the other manipulated input SRT also affects the required aeration. Figure 2(b) shows that a higher SRT reduces aeration costs to yield the same effluent quality at the expense of decreasing the production rate.

Cost index (IC)

This index computes the total energy (aeration EQa, pumping Epump and mixing Emix) employed in the ATAD reactor per unit of treated volume (Zambrano et al. 2009). The index is normalized as a percentage of an average energy requirement Eref = 12 kWh/m3sludge, extracted from USEPA 1990 for Fuchs systems 
formula
3

Production rate

Production rate is conditioned by the SRT with the consequences previously described. The population lifestyle and changes in environmental conditions modify drastically the inlet volume to the wastewater treatment plant, and consequently, the sludge line composition. Furthermore, raw sludge could come from other plants. At the same time, the production rate is limited by the ATAD capacity (digester and pre-holding tank volumes). Then, when the inlet volume to the sludge line is close to or slightly above the pre-holding tank capacity, a maximum production rate is compulsory to avoid overflows despite operating cost increases. Furthermore, there is also a minimum volume for ATAD filling.

Production index (IP)

This percentage index expresses the daily ratio between the volume of treated sludge and the maximum volume that can be treated. 
formula
4
IP is a reliable index only if the ATAD is properly operated. For example, an overflow event in the pre-holding tank would involve the ATAD being operated at full-capacity, giving a maximum IP. However, part of the raw sludge could not be properly treated.

Product quality

In terms of environmental policies, sludge quality can be evaluated through comparison of several parameters before and after treatment. In general, each environmental regulation establishes its own criteria and limits on certain parameters. Furthermore, the use of separate criteria to evaluate effluent quality in terms of reduction of organic matter (stabilization) and in terms of pathogen reduction (pasteurization) is widely recognized. A major Tavg promotes both sludge stabilization and pasteurization at the expense of an aeration cost increase.

Quality index (IQ)

This index computes a combination of stabilization index (IQST) and pasteurization index (IQPA). 
formula
5

Stabilization index

One of the most popular stabilization recommendations is the US Environmental Protection Agency (EPA) regulation 40 Code of Federal Regulations (CFR) Part 503 (USEPA 1993), which approves ‘at least a 38% reduction in VS during sewage’. This criterion is included in the following index: 
formula
6
IQST = 100% means a 38% reduction in VS, and then, full agreement with the regulation.

Pasteurization index

The EU recommendation (European Commission 2000) for pasteurization advises keeping the sludge over 55 °C for at least 20 h. Other regulations involve more requirements; for instance, (USEPA 1990) establishes a minimum time D[days] required as a function of the temperature Ti [°C], which is expressed by D = 50,070,000/100.14Ti. In this work, the pasteurization index is given by 
formula
7
where Ts is the sampling time [h] of temperature records, and N is the number of samples in a batch. IQPA = 100% means full agreement with the regulation.

Note that previous indexes IQST and IQPA not only evaluate whether the regulation criteria are met, but also their degree of compliance. They could even take values over 100%, which would point out effluent qualities beyond the regulation requirements and unnecessary expenses. To avoid the exponential growth of IQPA over 100%, a piecewise function is defined in Equation (5).

ATAD CONTROL STRATEGIES

Tradeoffs for simultaneous meeting of control goals

Since the best performance for the three aforementioned goals cannot be simultaneously achieved, several tradeoff combinations are proposed (see Table 1), which define the most common control strategies in ATAD. Attended goals will cause unavoidable side effects to non-attended goals. In automatic control terminology, Qa and SRT are the manipulated variables, Tavg is the controlled variable, and the unknown quality of inlet sludge (Xs,in among others) is the disturbance input. Notable points of the control strategy arrangement are:

  • The highest Tavg assures maximum effluent quality. It maximizes both the stabilization (IQST) and pasteurization (IQPA) indices, which take values over 100%. Then, IQ is the best.

  • Decreasing SRT ensures higher production because it is directly related to the volume treated per batch.

  • For a given SRT, it is possible to pursue either best quality, which looks for the highest possible Tavg (optimal aerated reaction), or just the quality that a specific regulation establishes, which saves aeration costs.

  • If the production rate is adapted to the raw sludge brought to the plant and if the best quality is attempted, the costs will depend on the SRT variation.

Table 1

Control strategies to meet specific goals (in bold) and their further side effects

Goals
 
  
Cost IC Quality IQ Production IP Control strategy Side effects 
Side effect Best Side effect Increase SRT to maximum and supply Qa to reach the highest Tavg Lowest production. Higher cost 
Lowest Side effect Highest Decrease SRT to minimum and supply the minimum Qa to meet the quality regulation IQ ≈ 100% 
Side effect Good Highest Decrease SRT to minimum and supply Qa to reach the highest Tavg Higher cost 
Goals
 
  
Cost IC Quality IQ Production IP Control strategy Side effects 
Side effect Best Side effect Increase SRT to maximum and supply Qa to reach the highest Tavg Lowest production. Higher cost 
Lowest Side effect Highest Decrease SRT to minimum and supply the minimum Qa to meet the quality regulation IQ ≈ 100% 
Side effect Good Highest Decrease SRT to minimum and supply Qa to reach the highest Tavg Higher cost 

This analysis offers heuristic rules to formulate the control specifications in an automatic control configuration, and it also provides quantitative tools (indices) to evaluate the performance of ATAD controllers. The specific design of these controllers and its online implementation are beyond the scope of this work. The following examples explain the usefulness of the analysis and indices.

Some simulation results

Three experiments are carried out with AT_BSM to illustrate the three control strategies listed in Table 1.

  • Experiment 1: This experiment looks for the best quality of the treated sludge, regardless of the other factors. Two actions are demanded for this purpose: optimum aeration level to reach the highest Tavg and maximum SRT (14 days) to enlarge the treatment time.

  • Experiment 2: This experiment looks for the lowest cost and a production rate as high as possible, always ensuring the minimum quality criteria. Two actions are demanded for this purpose: minimizing both the aeration level to achieve the minimum quality requirement and the SRT (10 days) to shorten the treatment time.

  • Experiment 3: This experiment looks for the highest production rate and a good-enough quality of the treated sludge. Two actions are demanded for this purpose: optimum aeration level to reach the highest Tavg and minimum SRT to shorten the treatment time.

A constant inlet composition of Xs.in = 20 kg/m3 is assumed for all the experiments. Suitable control inputs are supplied to meet each control strategy. The SRT range is about 10–14 days according to Scisson (2003). AT_BSM is simulated for 150 batches in order to reach a long-enough stationary regime. The experiment results in Table 2 are computed with the mean of the last 50 batches.

Table 2

Indices evaluation for the control strategies in Table 1

Experiment Evaluation indices
 
Cost IC Quality IQ Production IP 
93.81 115.01 63.94 
74.41 101.22 91.54 
91.20 111.17 92.56 
Experiment Evaluation indices
 
Cost IC Quality IQ Production IP 
93.81 115.01 63.94 
74.41 101.22 91.54 
91.20 111.17 92.56 

It is worth noting that the imposition on the quality index IQ (the highest IQPA and IQST given by Equations (6) and (7)) for Experiment 1, requires larger retention times and a reduction in the sludge production (lower IP). Moreover, this situation increases the costs (higher IC) because increasing the pasteurization index involves increasing the air supply. However, when a low IC and a high IP are mandatory (Experiment 2), it involves a decrease in the compliance of the effluent quality (low IQ) when compared with Experiment 1. Note that the highest production (Experiment 3) increases the cost, which reaches similar values to the cost for the best effluent quality (Experiment 1). The numeric values in Table 2 are representative of the plant performance. These values prove the theoretical relationships between the main control goals and validate the strategies proposed.

CONCLUSIONS

This work has shown a relevant analysis of ATAD behavior under different batch-mode operating conditions; a BSM was used. The manipulation of the air-flow rate and the sludge retention time were analyzed to provide high enough temperatures inside the digester. The evolution of biochemical variables and of organic matter indicators supported the use of temperature as a reliable online variable to track not only the pasteurization, but also the stabilization levels. In addition to improving sludge quality, other control goals included saving aeration energy or achieving an adequate rate of treated sludge. Then, performance indices were defined to quantify the quality, production and cost of ATAD management. Since all of these cannot be simultaneously achieved, several tradeoff control strategies were proposed. These statements constitute a guide for ATAD plant operators and help in the definition of specifications for the design of automatic controllers. The indices provide a tool to evaluate and compare control performance. Some examples showed the usefulness of the results.

ACKNOWLEDGMENT

The authors thank La Rioja Government for the financial support (project IMPULSA 2010/01 and Scholarship PhD program of S. Nájera) of the present work.

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