Energy Efficiency Optimisation of Wastewater ... - Semantic Scholar
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD Jaime Rojas Supervisor: Prof. Toshko Zhelev
Funded by
Outline
Our Starting Point Introduction Motivation and Aim Methodology Results Conclusion Future Work
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
2
Our Starting Point Ill-defined problem: energy efficiency improvement – But, how? Where to start? NH3 scrubber
2 Feed Tanks
Sludge from on-site WWTP
Biofilter
Belt Press Reactor Picket Fence Thickener
Reactor
1A
2A
(40-55oC)
(55-65oC)
4 Product Storage tanks
Fig. 1. Simplified flow diagram of the case study ATAD plant (Killarney, Co. Kerry, Ireland).
Fig. 2. Energy breakdown of the case study ATAD Plant.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
3
Introduction What is autothermal thermophilic aerobic digestion (ATAD)? Activated sludge process o Stabilisation o Pasteurisation
How ATAD works (more>>)
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
4
Introduction What is autothermal thermophilic aerobic digestion (ATAD)? Activated sludge process o Stabilisation o Pasteurisation
How ATAD works (more>>) Energy intensive1: 9-15 kWh/m3, 0.3-0.5 kWh/kg Conflicting reports regarding energy efficiency and cost effectiveness of ATAD systems2
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
5
Motivation and Aim Operating conditions unexploited • Invariable air supply3 and need for best aeration strategy4 • Influence of volume change frequency on energy requirement, but one single volume change per day: thermophiles’ efficiency not completely exploited5,6 • Sludge pre-heating: shorter reaction times2 • Shorter reaction times: avoidance of oxygen-limited conditions, reduction of thermal shock, and greater realizable load7
Need to identify optimum operating conditions of ATAD systems4,8 Aim: To minimize the energy requirement of ATAD systems by altering the operating conditions while complying with treatment goals
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
6
Methodology Dynamic optimisation9,10,11 tf
P(t ) min E [ x , u ] m t min dt u ( t ), t f 0 subject to f ( x , x , u ) 0 x (t0 ) x0
dynamic constraint
0.38 rVS (t f ) 0
stabilisation constraint
1 LP (t f ) 0 u L u (t ) uU
pasteurisation constraint
Before solving DO problem: 1.Quantification of treatment goals rVS and LP 2. ATAD model f 3. Selection of optimisation variables u
Em specific energy use [kWh/kg], cm specific plant capacity [kg/d], PL pasteurization lethality [%], rVS volatile solids reduction [%], x state variables , u optimization variables, P pasteurization lethality [%], rVS volatile solids (VS) reduction [%],
VS desired VS reduction level, t P pasteurization time [d], X VS (t ) VS solids concentration in the reactor at time t [kg/m 3 ], X VSfeed VS solids concentration in the feed [kg/m 3 ]
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
7
Methodology Quantification of treatment goals • Pasteurisation12 D
50070000 for TP 50 o C and D 0.021 days (30 min) 0.14TP 10 t
LP (t )
dt ' D t '0
1 LP (t f ) 0
tP
• Stabilisation rVS (t ) 1
X VS (t ) X VSfeed
0.38 rVS (t f ) 0
tr max{ t P , t S }
tS Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
8
Methodology Large Model13 • • •
Volume Mass balance (9 variables) Energy balance
dV qin qout dt d(VX i ) vij ρ jV + In(X i ) Out(X i ) dt j dH In( H l ) Out ( H l ) Pbio Pmix Pwall dt in out ...In( H g ) Out ( H g ) Qlat Qlat
V – Volume (m3) X i– Mass component i (g/l) H – Enthalpy (MJ)
Reduced Model14 • •
Mass balance (3 variables) Energy balance
d XB G bX B Q ( X Bfeed X B ) dt d X G b(1 f ) X Q ( X feed X ) dt dS A( S S ) G Q ( S feed S ) dt dT G h Q (T feed T ) dt
Xs – Biomass (g/l) X – Substrate (g/l)
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
S – Oxygen (mg/l) T – Temperature (°C)
9
Table 1. Petersen matrix of extended ASM1 model at thermophilic temperatures13,20
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
10
Methodology Calculation of specific energy requirements and plant capacity13 tf
Gravimetric
P(t ) dt min
cm
mVS tr
P(t ) EV dt Vin t0
cV
Vin tr
Em t0 tf
Volumetric
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
11
Results Simulation and verification – reduced model14
Fig. 6. Simulation of data from a full-scale 300 m3 ATAD reactor during a period of three weeks using the reduced model14: (a) Reactor temperature and (b) volatile solids.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
12
Results
0.65
0.65
0.6
0.6
0.55
0.55
0.5
0.5
Em [kWh/m 3]
Em [kWh/m 3]
Sensitivity analysis13
0.45 0.4
0.45 0.4
0.35
0.35
0.3
0.3
0.25
3
3.5 4 4.5 Aeration flowrate [vvh]
5
0.25 50
100
150 200 Loading time [min]
250
Fig. 8. Results from the sensitivity analysis for 1000 scenarios under different operating conditions and reactor volumes for a single reactor system showing effect of several variables on the energy requirement: (a) aeration flowrate, (b) loading time, (c) percent of volume replaced, and (d) reactor volume.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
13
Results Sensitivity analysis13 (a)
(b)
Fig. 9. Results from the sensitivity analysis for 1000 scenarios under different operating conditions and reactor volumes for a single reactor system displaying (a) the volumetric energy requirement as a function of plant capacity, and (b) the stabilization time vs. pasteurization time.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
14
Results Preliminary optimization results – Case study I Table 2. Design and operating parameters of . case study 1 (Killarney, Ireland). Parameter
Value
No. of reactors in series (-)
2
Reactor volume (m3)
100
Energy requirement (kWh/kg VS)
1.3
Daily load (m3/day)
20-35
Batch time (hours)
24
Hydraulic retention time (days)
7-10
Specific power (W/m3)
100
Aeration flowrate (vvh)
3-4
Influent VS concentration (g/l)
40
Influent temperature (°C)
10-17
Temperature in first reactor (°C)
40-50
Temperature in second reactor (°C)
50-65
Fig. 10. Optimal trajectories of state variables for case study 1 for nb-value of seven: (a) oxygen concentration, (b ) readily biodegradable substrate concentration, (c) thermophilic biomass concentration, and (d) temperature.
→ f* = 0.86 kWh/kg (33% improvement) Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
15
Results Preliminary optimization results – Case study II Table 3. Design and operating parameters of case study 2 (Navarra, Spain). Parameter
Value
No. of reactors in series (-)
1
Reactor volume (m3)
300
Energy requirement (kWh/kg VS)
2
Daily load (m3/day)
25-60
Batch time (hours)
24
Hydraulic retention time (days)
5-12
Specific power (W/m3)
120
Aeration flowrate (vvh)
0.8-1.4
Influent VS concentration (g/l)
30-37
Influent temperature (°C)
10-17
Reactor temperature (°C)
45-65
Fig. 11. Optimal trajectories of state variables for case study 2: (a) reactor temperature, (b) biomass concentration, (c) substrate concentration, and (d) oxygen concentration.
→ f* = 1.2 kWh/kg (40% improvement) Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
16
Conclusion Two ATAD models13,14 • Simulation studies • Verification Sensitivity analysis: identification of significant variables13 (reactor volume, aeration and sludge flowrate, and reaction time) • Reaction limited by stabilization • Substantial scope for improvement in terms of energy requirement and plant capacity by altering operating conditions General formulation and implementation of energy efficiency optimisation of ATAD systems • Case study I: 33% improvement • Case study II: 40% improvement
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
17
Future Work Implementation of optimisation problem using global optimisation techniques15,16 Formulation and implementation of a general scheduling and structural optimisation problem of ATAD systems17
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
18
References [1] USEPA (1990) ‘Environmental regulations and technology: Autothermal thermophilic aerobic digestion of municipal wastewater sludge’. Technical report, United States Environmental Protection Agency, Office of Research and Development, Washington, D.C. [2] Layden, N. M., Kelly, H. G., Mavinic, D. S., Moles, R. and Barlet, J. (2007b) 'Autothermal thermophilic aerobic digestion (ATAD) -- Part II: Review of research and full-scale operating experiences'. Journal of Environmental Engineering and Science, 6(6), 679-690. [3] Scisson, J. P. (2003) 'ATAD, The Next Generation: Design, Construction, Start-up and Operation of the First Municipal 2nd Generation ATAD', Joint Residuals and Biosolids Managament Conference and Exhibition 2003. [4] LaPara, T. M. and Alleman, J. E. (1999) 'Thermophilic aerobic biological wastewater treatment'. Water Research, 33(4), 895-908. [5] Ponti, C., Sonnleitner, B. and Fiechter, A. (1995a) 'Aerobic thermophilic treatment of sewage sludge at pilot plant scale. 1. Operating conditions'. Journal of Biotechnology, 38(2), 173-182. [6] Ponti, C., Sonnleitner, B. and Fiechter, A. (1995b) 'Aerobic thermophilic treatment of sewage sludge at pilot plant scale. 2. Technical solutions and process design'. Journal of Biotechnology, 38(2), 183-192. [7] Csikor, Z., Mihaltz, P., Hanifa, A., Kovacs, R. and Dahab, M. F. (2002) 'Identification of Factors Contributing to Degradation in Autothermal Thermophilic Sludge Digestion'. Water Science and Technology, 46(10), 131-8. [8] Layden, N. M., Kelly, H. G., Mavinic, D. S., Moles, R. and Barlet, J. (2007a) 'Autothermal thermophilic aerobic digestion (ATAD) -- Part I: Review of origins, design, and process operation'. Journal of Environmental Engineering and Science, 6(6), 665-678. [9] Pontryagin, L. S. (1964) ‘Mathematische Theorie Optimaler Prozesse’, Oldenbourg Verlag, Wien. [10] J.R. Banga, E. Balsa-Canto, C.G. Moles (2003), Dynamic optimization of bioreactors – a review, Proc. Ind. Natl. Sci. Acad. 69A, pp. 257-265. [11] D. Bonvin, S. Palanki and, B. Srinivasan (2003) Dynamic Optimization of Batch Processes: I. Characterization of the nominal solution, Computers and Chemical Engineering, vol. 27, pp. 1–26. [12] Toledo R T. (1991) ‘Fundamentals of Food Process Engineering’, 2nd ed. Chapman and Hall: New York. [13] Rojas, J., Zhelev, T., & Bojarski, A. D. (2010a) 'Modelling and Sensitivity Analysis of ATAD', Computers and Chemical Engineering, vol. 34, issue 5, pp. 802-811. doi:10.1016/j.compchemeng.2009.11.019 [14] Rojas, J., Burke, M., Chapwanya, M., Doherty, K., Hewitt, I., Korobeinikov, A., McCarthy, S ., Meere, M., Tuoi, V., O’Brien, M., Winstanley, H., Zhelev. T. (2010b) 'Modeling of autothermal thermophilic aerobic digestion' Mathematics-in-Industry Case Studies (MICS) Journal. vol. 2, pp. 34-63. [15] Rojas, J., Zhelev, T., Graells, M. ‘Energy efficiency optimization of wastewater treatment: study of ATAD’ Accepted for the 20th European Symposium of Computer Aided Process Engineering [16] Rojas, J. and Zhelev, T. ‘Taylor-made energy efficiency optimization of an ATAD plant’ Accepted for the 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. [17] Capon-Garcia, E., Rojas, L., Zhelev, T., Graells, M., ‘Operation Scheduling of Batch Autothermal Thermophilic Aerobic Digestion Processes’ Accepted for the 20th European Symposium of Computer Aided Process Engineering [18] Gomez, J., de Gracia, M., Ayesa, E. and Garcia-Heras, J. L. (2007) 'Mathematical modelling of autothermal thermophilic aerobic digesters'. Water Research, 41(5), 959-968. [19] Kambhu, K. and Andrews, J. F. (1969) 'Aerobic Thermophilic Process for the Biological Treatment of Wastes - Simulation Studies'. Journal - Water Pollution Control Federation, 41(127-143. [20] Kovacs, R. and Mihaltz, P. (2005) 'Untersuchungen der Kinetik der aerob-thermophilen Klaerschlammstabilisierung - Einfluss der Temperatur', 17. Fruehlingsakademie und Expertentagung, Balatonfuered, Germany, May 2005.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
19
Funded by
Outline
Our Starting Point Introduction Motivation and Aim Methodology Results Conclusion Future Work
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
2
Our Starting Point Ill-defined problem: energy efficiency improvement – But, how? Where to start? NH3 scrubber
2 Feed Tanks
Sludge from on-site WWTP
Biofilter
Belt Press Reactor Picket Fence Thickener
Reactor
1A
2A
(40-55oC)
(55-65oC)
4 Product Storage tanks
Fig. 1. Simplified flow diagram of the case study ATAD plant (Killarney, Co. Kerry, Ireland).
Fig. 2. Energy breakdown of the case study ATAD Plant.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
3
Introduction What is autothermal thermophilic aerobic digestion (ATAD)? Activated sludge process o Stabilisation o Pasteurisation
How ATAD works (more>>)
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
4
Introduction What is autothermal thermophilic aerobic digestion (ATAD)? Activated sludge process o Stabilisation o Pasteurisation
How ATAD works (more>>) Energy intensive1: 9-15 kWh/m3, 0.3-0.5 kWh/kg Conflicting reports regarding energy efficiency and cost effectiveness of ATAD systems2
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
5
Motivation and Aim Operating conditions unexploited • Invariable air supply3 and need for best aeration strategy4 • Influence of volume change frequency on energy requirement, but one single volume change per day: thermophiles’ efficiency not completely exploited5,6 • Sludge pre-heating: shorter reaction times2 • Shorter reaction times: avoidance of oxygen-limited conditions, reduction of thermal shock, and greater realizable load7
Need to identify optimum operating conditions of ATAD systems4,8 Aim: To minimize the energy requirement of ATAD systems by altering the operating conditions while complying with treatment goals
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
6
Methodology Dynamic optimisation9,10,11 tf
P(t ) min E [ x , u ] m t min dt u ( t ), t f 0 subject to f ( x , x , u ) 0 x (t0 ) x0
dynamic constraint
0.38 rVS (t f ) 0
stabilisation constraint
1 LP (t f ) 0 u L u (t ) uU
pasteurisation constraint
Before solving DO problem: 1.Quantification of treatment goals rVS and LP 2. ATAD model f 3. Selection of optimisation variables u
Em specific energy use [kWh/kg], cm specific plant capacity [kg/d], PL pasteurization lethality [%], rVS volatile solids reduction [%], x state variables , u optimization variables, P pasteurization lethality [%], rVS volatile solids (VS) reduction [%],
VS desired VS reduction level, t P pasteurization time [d], X VS (t ) VS solids concentration in the reactor at time t [kg/m 3 ], X VSfeed VS solids concentration in the feed [kg/m 3 ]
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
7
Methodology Quantification of treatment goals • Pasteurisation12 D
50070000 for TP 50 o C and D 0.021 days (30 min) 0.14TP 10 t
LP (t )
dt ' D t '0
1 LP (t f ) 0
tP
• Stabilisation rVS (t ) 1
X VS (t ) X VSfeed
0.38 rVS (t f ) 0
tr max{ t P , t S }
tS Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
8
Methodology Large Model13 • • •
Volume Mass balance (9 variables) Energy balance
dV qin qout dt d(VX i ) vij ρ jV + In(X i ) Out(X i ) dt j dH In( H l ) Out ( H l ) Pbio Pmix Pwall dt in out ...In( H g ) Out ( H g ) Qlat Qlat
V – Volume (m3) X i– Mass component i (g/l) H – Enthalpy (MJ)
Reduced Model14 • •
Mass balance (3 variables) Energy balance
d XB G bX B Q ( X Bfeed X B ) dt d X G b(1 f ) X Q ( X feed X ) dt dS A( S S ) G Q ( S feed S ) dt dT G h Q (T feed T ) dt
Xs – Biomass (g/l) X – Substrate (g/l)
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
S – Oxygen (mg/l) T – Temperature (°C)
9
Table 1. Petersen matrix of extended ASM1 model at thermophilic temperatures13,20
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
10
Methodology Calculation of specific energy requirements and plant capacity13 tf
Gravimetric
P(t ) dt min
cm
mVS tr
P(t ) EV dt Vin t0
cV
Vin tr
Em t0 tf
Volumetric
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
11
Results Simulation and verification – reduced model14
Fig. 6. Simulation of data from a full-scale 300 m3 ATAD reactor during a period of three weeks using the reduced model14: (a) Reactor temperature and (b) volatile solids.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
12
Results
0.65
0.65
0.6
0.6
0.55
0.55
0.5
0.5
Em [kWh/m 3]
Em [kWh/m 3]
Sensitivity analysis13
0.45 0.4
0.45 0.4
0.35
0.35
0.3
0.3
0.25
3
3.5 4 4.5 Aeration flowrate [vvh]
5
0.25 50
100
150 200 Loading time [min]
250
Fig. 8. Results from the sensitivity analysis for 1000 scenarios under different operating conditions and reactor volumes for a single reactor system showing effect of several variables on the energy requirement: (a) aeration flowrate, (b) loading time, (c) percent of volume replaced, and (d) reactor volume.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
13
Results Sensitivity analysis13 (a)
(b)
Fig. 9. Results from the sensitivity analysis for 1000 scenarios under different operating conditions and reactor volumes for a single reactor system displaying (a) the volumetric energy requirement as a function of plant capacity, and (b) the stabilization time vs. pasteurization time.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
14
Results Preliminary optimization results – Case study I Table 2. Design and operating parameters of . case study 1 (Killarney, Ireland). Parameter
Value
No. of reactors in series (-)
2
Reactor volume (m3)
100
Energy requirement (kWh/kg VS)
1.3
Daily load (m3/day)
20-35
Batch time (hours)
24
Hydraulic retention time (days)
7-10
Specific power (W/m3)
100
Aeration flowrate (vvh)
3-4
Influent VS concentration (g/l)
40
Influent temperature (°C)
10-17
Temperature in first reactor (°C)
40-50
Temperature in second reactor (°C)
50-65
Fig. 10. Optimal trajectories of state variables for case study 1 for nb-value of seven: (a) oxygen concentration, (b ) readily biodegradable substrate concentration, (c) thermophilic biomass concentration, and (d) temperature.
→ f* = 0.86 kWh/kg (33% improvement) Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
15
Results Preliminary optimization results – Case study II Table 3. Design and operating parameters of case study 2 (Navarra, Spain). Parameter
Value
No. of reactors in series (-)
1
Reactor volume (m3)
300
Energy requirement (kWh/kg VS)
2
Daily load (m3/day)
25-60
Batch time (hours)
24
Hydraulic retention time (days)
5-12
Specific power (W/m3)
120
Aeration flowrate (vvh)
0.8-1.4
Influent VS concentration (g/l)
30-37
Influent temperature (°C)
10-17
Reactor temperature (°C)
45-65
Fig. 11. Optimal trajectories of state variables for case study 2: (a) reactor temperature, (b) biomass concentration, (c) substrate concentration, and (d) oxygen concentration.
→ f* = 1.2 kWh/kg (40% improvement) Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
16
Conclusion Two ATAD models13,14 • Simulation studies • Verification Sensitivity analysis: identification of significant variables13 (reactor volume, aeration and sludge flowrate, and reaction time) • Reaction limited by stabilization • Substantial scope for improvement in terms of energy requirement and plant capacity by altering operating conditions General formulation and implementation of energy efficiency optimisation of ATAD systems • Case study I: 33% improvement • Case study II: 40% improvement
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
17
Future Work Implementation of optimisation problem using global optimisation techniques15,16 Formulation and implementation of a general scheduling and structural optimisation problem of ATAD systems17
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
18
References [1] USEPA (1990) ‘Environmental regulations and technology: Autothermal thermophilic aerobic digestion of municipal wastewater sludge’. Technical report, United States Environmental Protection Agency, Office of Research and Development, Washington, D.C. [2] Layden, N. M., Kelly, H. G., Mavinic, D. S., Moles, R. and Barlet, J. (2007b) 'Autothermal thermophilic aerobic digestion (ATAD) -- Part II: Review of research and full-scale operating experiences'. Journal of Environmental Engineering and Science, 6(6), 679-690. [3] Scisson, J. P. (2003) 'ATAD, The Next Generation: Design, Construction, Start-up and Operation of the First Municipal 2nd Generation ATAD', Joint Residuals and Biosolids Managament Conference and Exhibition 2003. [4] LaPara, T. M. and Alleman, J. E. (1999) 'Thermophilic aerobic biological wastewater treatment'. Water Research, 33(4), 895-908. [5] Ponti, C., Sonnleitner, B. and Fiechter, A. (1995a) 'Aerobic thermophilic treatment of sewage sludge at pilot plant scale. 1. Operating conditions'. Journal of Biotechnology, 38(2), 173-182. [6] Ponti, C., Sonnleitner, B. and Fiechter, A. (1995b) 'Aerobic thermophilic treatment of sewage sludge at pilot plant scale. 2. Technical solutions and process design'. Journal of Biotechnology, 38(2), 183-192. [7] Csikor, Z., Mihaltz, P., Hanifa, A., Kovacs, R. and Dahab, M. F. (2002) 'Identification of Factors Contributing to Degradation in Autothermal Thermophilic Sludge Digestion'. Water Science and Technology, 46(10), 131-8. [8] Layden, N. M., Kelly, H. G., Mavinic, D. S., Moles, R. and Barlet, J. (2007a) 'Autothermal thermophilic aerobic digestion (ATAD) -- Part I: Review of origins, design, and process operation'. Journal of Environmental Engineering and Science, 6(6), 665-678. [9] Pontryagin, L. S. (1964) ‘Mathematische Theorie Optimaler Prozesse’, Oldenbourg Verlag, Wien. [10] J.R. Banga, E. Balsa-Canto, C.G. Moles (2003), Dynamic optimization of bioreactors – a review, Proc. Ind. Natl. Sci. Acad. 69A, pp. 257-265. [11] D. Bonvin, S. Palanki and, B. Srinivasan (2003) Dynamic Optimization of Batch Processes: I. Characterization of the nominal solution, Computers and Chemical Engineering, vol. 27, pp. 1–26. [12] Toledo R T. (1991) ‘Fundamentals of Food Process Engineering’, 2nd ed. Chapman and Hall: New York. [13] Rojas, J., Zhelev, T., & Bojarski, A. D. (2010a) 'Modelling and Sensitivity Analysis of ATAD', Computers and Chemical Engineering, vol. 34, issue 5, pp. 802-811. doi:10.1016/j.compchemeng.2009.11.019 [14] Rojas, J., Burke, M., Chapwanya, M., Doherty, K., Hewitt, I., Korobeinikov, A., McCarthy, S ., Meere, M., Tuoi, V., O’Brien, M., Winstanley, H., Zhelev. T. (2010b) 'Modeling of autothermal thermophilic aerobic digestion' Mathematics-in-Industry Case Studies (MICS) Journal. vol. 2, pp. 34-63. [15] Rojas, J., Zhelev, T., Graells, M. ‘Energy efficiency optimization of wastewater treatment: study of ATAD’ Accepted for the 20th European Symposium of Computer Aided Process Engineering [16] Rojas, J. and Zhelev, T. ‘Taylor-made energy efficiency optimization of an ATAD plant’ Accepted for the 23rd International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems. [17] Capon-Garcia, E., Rojas, L., Zhelev, T., Graells, M., ‘Operation Scheduling of Batch Autothermal Thermophilic Aerobic Digestion Processes’ Accepted for the 20th European Symposium of Computer Aided Process Engineering [18] Gomez, J., de Gracia, M., Ayesa, E. and Garcia-Heras, J. L. (2007) 'Mathematical modelling of autothermal thermophilic aerobic digesters'. Water Research, 41(5), 959-968. [19] Kambhu, K. and Andrews, J. F. (1969) 'Aerobic Thermophilic Process for the Biological Treatment of Wastes - Simulation Studies'. Journal - Water Pollution Control Federation, 41(127-143. [20] Kovacs, R. and Mihaltz, P. (2005) 'Untersuchungen der Kinetik der aerob-thermophilen Klaerschlammstabilisierung - Einfluss der Temperatur', 17. Fruehlingsakademie und Expertentagung, Balatonfuered, Germany, May 2005.
Energy Efficiency Optimisation of Wastewater Treatment: Study of ATAD
19
