Asm2d Phosphorus

ADVANTAGES3 · dim 19

SolvSRK wins. At the comparison noise level, SolvSRK beats the best baseline by at least 10 percentage points of survival, or by at least 0.05 balanced score when survival is tied. Use SolvSRK for this class of problem. All verdicts →

IWA Activated Sludge Model No. 2d: 19-state extension of ASM1 with biological phosphorus removal via PAOs. Adds PHA storage, aerobic PAO growth, poly-P storage, and lysis. Multi-species with internal storage dynamics creating additional fast modes against slow biomass background.

Water treatment & environmental

Problem definition

Canonical benchmark implementation

Canonical RHS excerpt from the registered callable used for this benchmark cell. Expand it to verify the state equations; it is not a standalone runnable fixture.

Show canonical RHS excerpt
def _monod(s, k):
    """Monod saturation term: s / (k + s), safe for s near zero."""
    return s / (k + s) if (k + s) > 0.0 else 0.0

def _asm1_process_rates(y):
    """Compute the 8 ASM1 biological process rates from state vector y.

    Returns array of 8 rates (rho_1 .. rho_8).
    """
    S_I, S_S, X_I, X_S, X_BH, X_BA, X_P = y[0], y[1], y[2], y[3], y[4], y[5], y[6]
    S_O, S_NO, S_NH, S_ND, X_ND, S_ALK = y[7], y[8], y[9], y[10], y[11], y[12]

    mon_ss = _monod(S_S, _K_S)
    mon_o_h = _monod(S_O, _K_OH)
    inh_o_h = _K_OH / (_K_OH + S_O) if (_K_OH + S_O) > 0.0 else 0.0
    mon_no = _monod(S_NO, _K_NO)
    mon_nh = _monod(S_NH, _K_NH)
    mon_o_a = _monod(S_O, _K_OA)

    # Process 1: aerobic growth of heterotrophs
    rho1 = _MU_H * mon_ss * mon_o_h * X_BH

    # Process 2: anoxic growth of heterotrophs
    rho2 = _MU_H * mon_ss * inh_o_h * mon_no * _ETA_G * X_BH

    # Process 3: aerobic growth of autotrophs
    rho3 = _MU_A * mon_nh * mon_o_a * X_BA

    # Process 4: decay of heterotrophs
    rho4 = _B_H * X_BH

    # Process 5: decay of autotrophs
    rho5 = _B_A * X_BA

    # Process 6: ammonification of soluble organic nitrogen
    rho6 = _K_A_AMMON * S_ND * X_BH

    # Process 7: hydrolysis of slowly biodegradable substrate
    xs_xbh_ratio = (X_S / X_BH) if X_BH > 1e-12 else 0.0
    mon_hyd = xs_xbh_ratio / (_K_X + xs_xbh_ratio) if (_K_X + xs_xbh_ratio) > 0.0 else 0.0
    hyd_switch = mon_o_h + _ETA_H * inh_o_h * mon_no
    rho7 = _K_H * mon_hyd * hyd_switch * X_BH

    # Process 8: hydrolysis of organic nitrogen
    xnd_xs_ratio = (X_ND / X_S) if X_S > 1e-12 else 0.0
    rho8 = rho7 * xnd_xs_ratio

    return np.array([rho1, rho2, rho3, rho4, rho5, rho6, rho7, rho8])

def _asm1_reaction_vector(rho):
    """Petersen matrix: convert 8 process rates to 13 state derivatives."""
    rho1, rho2, rho3, rho4, rho5, rho6, rho7, rho8 = rho

    dy = np.zeros(13)

    # dS_I/dt   = 0 (inert, only dilution)
    # dS_S/dt   = -(1/Y_H)*rho1 - (1/Y_H)*rho2 + rho7
    dy[1] = -(1.0 / _Y_H) * rho1 - (1.0 / _Y_H) * rho2 + rho7

    # dX_I/dt   = 0 (inert particulate, only dilution)
    # dX_S/dt   = (1-f_p)*rho4 + (1-f_p)*rho5 - rho7
    dy[3] = (1.0 - _F_P) * rho4 + (1.0 - _F_P) * rho5 - rho7

    # dX_BH/dt  = rho1 + rho2 - rho4
    dy[4] = rho1 + rho2 - rho4

    # dX_BA/dt  = rho3 - rho5
    dy[5] = rho3 - rho5

    # dX_P/dt   = f_p*rho4 + f_p*rho5
    dy[6] = _F_P * rho4 + _F_P * rho5

    # dS_O/dt   = -((1-Y_H)/Y_H)*rho1 - ((4.57-Y_A)/Y_A)*rho3 + KLa*(S_O_sat - S_O)
    #             (aeration handled separately in the full RHS)
    dy[7] = -((1.0 - _Y_H) / _Y_H) * rho1 - ((4.57 - _Y_A) / _Y_A) * rho3

    # dS_NO/dt  = -((1-Y_H)/(2.86*Y_H))*rho2 + (1/Y_A)*rho3
    dy[8] = -((1.0 - _Y_H) / (2.86 * _Y_H)) * rho2 + (1.0 / _Y_A) * rho3

    # dS_NH/dt  = -i_XB*rho1 - i_XB*rho2 - (i_XB + 1/Y_A)*rho3 + rho6
    dy[9] = -_I_XB * rho1 - _I_XB * rho2 - (_I_XB + 1.0 / _Y_A) * rho3 + rho6

    # dS_ND/dt  = -rho6 + rho8
    dy[10] = -rho6 + rho8

    # dX_ND/dt  = (i_XB - f_p*i_XP)*rho4 + (i_XB - f_p*i_XP)*rho5 - rho8
    dy[11] = (_I_XB - _F_P * _I_XP) * rho4 + (_I_XB - _F_P * _I_XP) * rho5 - rho8

    # dS_ALK/dt = -(i_XB/14)*rho1 + ((1-Y_H)/(14*2.86*Y_H))*rho2
    #             - (i_XB/14 + 1/(7*Y_A))*rho3 + rho6/14
    dy[12] = (
        -(_I_XB / 14.0) * rho1
        + ((1.0 - _Y_H) / (14.0 * 2.86 * _Y_H)) * rho2
        - (_I_XB / 14.0 + 1.0 / (7.0 * _Y_A)) * rho3
        + rho6 / 14.0
    )

    return dy

def _nonneg_clamp(y, dy):
    """IWA-standard non-negativity enforcement at the RHS level.

    If a state is at (or below) zero and the derivative would push it
    further negative, clamp the derivative to zero.  This prevents
    physically impossible negative concentrations without modifying the
    solver.
    """
    for i in range(len(y)):
        if y[i] <= 0.0 and dy[i] < 0.0:
            dy[i] = 0.0
    return dy

def _rhs_asm2d(t, y):
    """ASM2d CSTR: ASM1 processes + PAO bio-P processes (19-state)."""
    y_safe = np.maximum(y, 0.0)

    # -- ASM1 base processes (on the first 13 states) --
    rho = _asm1_process_rates(y_safe[:13])
    r_asm1 = _asm1_reaction_vector(rho)

    # -- PAO-specific states --
    S_O = y_safe[7]
    S_NH = y_safe[9]
    S_A = y_safe[13]
    S_PO4 = y_safe[15]
    X_PAO = y_safe[16]
    X_PHA = y_safe[17]
    X_PP = y_safe[18]

    # safe ratios to avoid division by zero
    pha_pao = (X_PHA / X_PAO) if X_PAO > 1e-12 else 0.0
    pp_pao = (X_PP / X_PAO) if X_PAO > 1e-12 else 0.0

    # Process: anaerobic PHA storage
    rho_sto = (_Q_PHA
               * _monod(S_A, _K_A_PAO)
               * (pp_pao / (_K_PP + pp_pao) if (_K_PP + pp_pao) > 0.0 else 0.0)
               * X_PAO)

    # Process: aerobic growth of PAO
    rho_pao_aer = (_MU_PAO
                   * _monod(S_O, _K_O_PAO)
                   * _monod(S_NH, _K_NH4)
                   * _monod(S_PO4, _K_P)
                   * (pha_pao / (_K_PHA + pha_pao) if (_K_PHA + pha_pao) > 0.0 else 0.0)
                   * X_PAO)

    # Process: aerobic poly-P storage
    kmax_minus_pp = max(_K_MAX - pp_pao, 0.0)
    rho_pp_aer = (_Q_PP
                  * _monod(S_O, _K_O_PAO)
                  * _monod(S_PO4, _K_PS)
                  * (pha_pao / (_K_PHA + pha_pao) if (_K_PHA + pha_pao) > 0.0 else 0.0)
                  * (kmax_minus_pp / (_K_IPP + kmax_minus_pp) if (_K_IPP + kmax_minus_pp) > 0.0 else 0.0)
                  * X_PAO)

    # Lysis processes (first-order)
    rho_lys_pao = _B_PAO * X_PAO
    rho_lys_pp = _B_PP * X_PP
    rho_lys_pha = _B_PHA * X_PHA

    # -- Build 19-state derivative --
    D_h = _D / _H_PER_D
    dy = np.zeros(19)

    # ASM1 base reactions (converted from d^-1 to h^-1)
    for i in range(13):
        dy[i] = r_asm1[i] / _H_PER_D + D_h * (_Y_IN_ASM2D[i] - y_safe[i])

    # Aeration for S_O
    dy[7] += (_KLA / _H_PER_D) * (_S_O_SAT - y_safe[7])

    # S_A (index 13): consumed by PHA storage
    dy[13] = D_h * (_Y_IN_ASM2D[13] - y_safe[13]) - rho_sto / _H_PER_D

    # S_F (index 14): dilution only (fermentation not modeled explicitly here)
    dy[14] = D_h * (_Y_IN_ASM2D[14] - y_safe[14])

    # S_PO4 (index 15): released during PHA storage, consumed during PP storage and PAO growth
    dy[15] = (D_h * (_Y_IN_ASM2D[15] - y_safe[15])
              + (_Y_PO4_PHA * rho_sto - rho_pp_aer - _Y_PP_PAO * rho_pao_aer
                 + rho_lys_pp) / _H_PER_D)

    # X_PAO (index 16): grows aerobically, decays
    dy[16] = (D_h * (_Y_IN_ASM2D[16] - y_safe[16])
              + (rho_pao_aer - rho_lys_pao) / _H_PER_D)

    # X_PHA (index 17): stored anaerobically, consumed for PAO growth, lyses
    dy[17] = (D_h * (_Y_IN_ASM2D[17] - y_safe[17])
              + (rho_sto - _Y_PHA_PAO * rho_pao_aer - rho_lys_pha) / _H_PER_D)

    # X_PP (index 18): stored aerobically, released anaerobically, lyses
    dy[18] = (D_h * (_Y_IN_ASM2D[18] - y_safe[18])
              + (rho_pp_aer - _Y_PO4_PHA * rho_sto - rho_lys_pp) / _H_PER_D)

    return _nonneg_clamp(y, dy)
Parameters
  • _B_A = 0.15
  • _B_H = 0.62
  • _B_PAO = 0.2
  • _B_PHA = 0.2
  • _B_PP = 0.2
  • _D = 0.0833333333333
  • _ETA_G = 0.8
  • _ETA_H = 0.4
  • _F_P = 0.08
  • _H_PER_D = 24
  • _I_XB = 0.086
  • _I_XP = 0.06
  • _KLA = 120
  • _K_A_AMMON = 0.08
  • _K_A_PAO = 4
  • _K_H = 3
  • _K_IPP = 0.02
  • _K_MAX = 0.34
  • _K_NH = 1
  • _K_NH4 = 0.05
  • _K_NO = 0.5
  • _K_OA = 0.4
  • _K_OH = 0.2
  • _K_O_PAO = 0.2
  • _K_P = 0.01
  • _K_PHA = 0.01
  • _K_PP = 0.01
  • _K_PS = 0.2
  • _K_S = 20
  • _K_X = 0.03
  • _MU_A = 0.8
  • _MU_H = 6
  • _MU_PAO = 1
  • _Q_PHA = 3
  • _Q_PP = 1.5
  • _S_O_SAT = 8
  • _Y_A = 0.24
  • _Y_H = 0.67
  • _Y_IN_ASM2D = [30, 69.5, 51.2, 202.3, 0, 0, …] [shape=(19,), min=0, max=202.3]
  • _Y_PHA_PAO = 1.5
  • _Y_PO4_PHA = 0.4
  • _Y_PP_PAO = 0.3
Initial condition
y(0) = [30, 5, 1000, 100, 2500, 150, …] [shape=(19,), min=1, max=2500]
Horizon
t ∈ [0, 168]

Canonical RHS excerpt captured from the same registered callable used for the published benchmark. Frozen closure values are summarized below; helper imports and solver settings are intentionally omitted.

Fingerprint

Spread: extreme

Default noise: low

Recommendation snapshot

Clean best: SolvSRK

Noisy best: SolvSRK

Coverage

14 solver arms · clean + 5 noise levels

Ranked on survival, precision, and speed

Versions & freeze

Methodology →
Freeze
2026-08-13
libsolvsrk
2.3.0
SciPy
1.14
SUNDIALS
CVODE (bundled backend)

20 seeds/cell default · 14 arms · TRL 4–5 · simulation-lab validated · this page: Asm2d Phosphorus (asm2d-phosphorus)

Governed SolvTune benchmark freeze; per-arm medians only. RHS definitions and raw trial rows are not published.

Self-reported by Resonix Labs · not independently verified

Results matrix

Pick an objective and a noise level to rank all arms on survival, median SCD, median nfev, and median wall time. Medians across seeds.

Objective

Best overall trade-off of survival, precision, and speed.

Noise level

#SolverSurvivalSCDnfevWallScore
1SolvSRK
100%
11.14,645134 ms0.884
2SciPy RadauSciPy
100%
10.66,559257 ms0.872
3SciPy RK23SciPy
100%
9.48,042253 ms0.844
4SciPy DOP853SciPy
100%
9.39,482268 ms0.841
5Tsit5external
100%
8.97,7941.58 s0.832
6SciPy LSODASciPy
100%
8.82,55966 ms0.828
7SciPy RK45SciPy
100%
8.88,078236 ms0.828
8SciPy BDFSciPy
100%
7.52,558129 ms0.796
9CVODE Adamsexternal
100%
7.31,67454 ms0.793
10CVODE BDFexternal
100%
6.91,48249 ms0.783

At Clean, best balanced arm is SolvSRK.

Values are medians across seeds, measured by Resonix Labs on Resonix hardware and not independently verified; nfev and wall are on reference lab hardware (indicative). Under injected noise only SolvSRK and the SciPy arms are run. How we measure accuracy → · Verification status →

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Cite this page

Replace the access date. Pin the freeze ID and library versions when comparing against a later export. Cite it as what it is — a self-reported vendor benchmark, not an independently verified result. The note field says so; please keep it.

@misc{resonix_evidence_asm2d_phosphorus_2026,
  title        = {Resonix Evidence Portal: Asm2d Phosphorus},
  author       = {{Resonix Labs (Canada) Inc.}},
  year         = {2026},
  howpublished = {\url{https://resonix.tech/evidence/problems/asm2d-phosphorus}},
  note         = {Self-reported vendor benchmark; internally generated by Resonix Labs and not independently verified. Accessed YYYY-MM-DD. Freeze 2026-08-13; libsolvsrk 2.3.0; SciPy 1.14.}
}

Related

TRL 4–5 · simulation-lab validated · 398 problems · 14 solver arms · clean + 5 noise levels

Freeze: 2026-08-13 · scipy 1.14 · libsolvsrk 2.3.0 · Methodology

Self-reported by Resonix Labs · not independently verified · Verification status