def __init__(self):
# action space: Continuous Do setpoints | range-> -1~1 (applicable variation of DO, need to be checked with the other Envs.
self.action_space = spaces.Box(
np.array([-1.0]), np.array([1.0]),
dtype=np.float32) # -1~1 으로 DO setpoint change
# state space: Continuous, components | normalized
self.observation_space = spaces.Box(low=0.9 * np.ones([14]),
high=np.ones([14]),
dtype=np.float32)
# SBR parameters
self.Spar = [0.24, 0.67, 0.08, 0.08, 0.06] # (ref. BSM1 report Tbl. 2)
# Ya Yh fp ixb ixp
self.Kpar = [
4.0, 10.0, 0.2, 0.5, 0.3, 0.8, 0.8, 3.0, 0.1, 0.5, 1.0, 0.05, 0.4,
0.05
] # (ref. BSM1 report Tbl. 3)
# muhath Ks Koh Kno bh tag etah kh Kx muhata Knh ba Koa Ka
self.DO_control_par = [
5.0, 0.00035, 0.02 / 24, 2, 0, 240, 12, 2, 5, 0.005,
DO_set(15)
]
self.biomass_setpoint = 2700
self.Qeff = 0.66
self.x_1 = np.array([
1.32000000e+00, 3.00000000e+01, 3.81606587e+01, 6.94658685e+02,
1.07772100e+02, 1.22613841e+03, 7.88460027e+01, 2.57616136e+02,
1.01108024e+00, 6.24510635e+00, 1.78877937e+01, 3.95743344e+00,
5.70432163e+00, 5.50185509e+00
])
def __init__(self):
# action space: Continuous Do setpoints | range-> -1~1 (applicable variation of DO, need to be checked with the other Envs.
self.action_space = spaces.Box(
np.array([-1]), np.array([1]),
dtype=np.float32) # -0.05~0.05 으로 DO setpoint change
# state space: Continuous, components | normalized
self.observation_space = spaces.Box(
low=np.array([0, 0, 0, -1, -1]),
high=np.ones([5]) * 1.0,
# low=np.array([0, 0,0,0,0,0,0]), high=np.ones([7])*1.0, #
dtype=np.float32) # 7가지 ASM components를 state로 선정함.
# SBR parameters
self.Spar = [0.24, 0.67, 0.08, 0.08,
0.06] # (ref. BSM1 report Tbl. 2) # BSM report에 따름
# Ya Yh fp ixb ixp
self.Kpar = [
4.0, 10.0, 0.2, 0.5, 0.3, 0.8, 0.8, 3.0, 0.1, 0.5, 1.0, 0.05, 0.4,
0.05
] # (ref. BSM1 report Tbl. 3) # BSM report에 따름
# muhath Ks Koh Kno bh tag etah kh Kx muhata Knh ba Koa Ka
self.DO_control_par = [
5.0, 0.00035, 0.02 / 24, 2, 0, 240, 12, 2, 5, 0.005,
DO_set(15)
] # DO controller의 control parameter
self.biomass_setpoint = 2700 # settling 시 reactor에 남겨지는 biomass 총량 값. => 이 값에 맞도록 Qw를 빼냄.
self.Qeff = 0.66
self.u_t_t = np.array(np.zeros([1, 466])) # 먼지잘 모르겠음.
"""
List of variables :
0: time, 2=Ss, 6=Xbh, 7=Xba, 9=So, 10=Sno, 11=Snh
(ref. BSM1 report Tbl. 1)
"""
self.state_idx = [
0, 8, 10
] # 각 변수에 따른 위치값 #self.state_idx = [0, 2, 5, 6, 8, 9, 10] #각 변수에 따른 위치값 #
self.x_1 = np.array(
[0.5, 8., 30]
) # Normalization을 위한 초기 값.self.x_1 = np.array([0.5, 5, 3000., 200., 8., 30., 30]) # Normalization을 위한 초기 값.
self.x_2 = np.zeros([1, len(self.state_idx)])
self.x_2 = np.zeros([1, len(self.state_idx)])
self.t_t = [] # DeepRL code와 결합시켜 plotting하기 위한 변수, time trajectory
self.x_t = [] # DeepRL code와 결합시켜 plotting하기 위한 변수, state trajectory
self.state_t = []
def __init__(self):
# action space: Continuous Do setpoints | range-> -1~1 (applicable variation of DO, need to be checked with the other Envs.
self.action_space = spaces.Box(
np.array([-0.05]), np.array([0.05]),
dtype=np.float32) # -0.05~0.05 으로 DO setpoint change
# state space: Continuous, components | normalized
self.observation_space = spaces.Box(low=np.array(
[0, 0.9, 0.9, 0.9, 0.9, 0.9, 0.9]),
high=np.ones([7]) * 1.3,
dtype=np.float32)
# SBR parameters
self.Spar = [0.24, 0.67, 0.08, 0.08, 0.06] # (ref. BSM1 report Tbl. 2)
# Ya Yh fp ixb ixp
self.Kpar = [
4.0, 10.0, 0.2, 0.5, 0.3, 0.8, 0.8, 3.0, 0.1, 0.5, 1.0, 0.05, 0.4,
0.05
] # (ref. BSM1 report Tbl. 3)
# muhath Ks Koh Kno bh tag etah kh Kx muhata Knh ba Koa Ka
self.DO_control_par = [
5.0, 0.00035, 0.02 / 24, 2, 0, 240, 12, 2, 5, 0.005,
DO_set(15)
]
self.biomass_setpoint = 2700
self.Qeff = 0.66
self.u_t_t = np.array(np.zeros([1, 466]))
"""
List of variables :
0: time, 2=Ss, 6=Xbh, 7=Xba, 9=So, 10=Sno, 11=Snh
(ref. BSM1 report Tbl. 1)
"""
self.state_idx = [0, 1, 5, 6, 8, 9, 10]
self.x_1 = np.array([0.5, 30, 2599., 168., 2., 13., 0.005])
self.x_2 = np.zeros([1, len(self.state_idx)])
WV = 1.32 # m^3, Working Volume
t_ratio = [
4.2 / 100, 8.3 / 100, 37.5 / 100, 31.2 / 100, 2.1 / 100, 8.3 / 100,
2.1 / 100, 6.3 / 100
]
t_delta = 0.002 / 24
# phase time
t_cycle = 12 / 24 # hour -> day, 12hr
t_memory1, t_memory2, t_memory3, t_memory4, t_memory5, t_memory8 = batch_time(
t_cycle, t_ratio, t_delta)
#Oxygen concentration at saturation : 15deg
So_sat = DO_set(15)
# DO control prarameters
DO_control_par = [5.0, 0.00035, 0.02 / 24, 2, 0, 240, 12, 2, 5, 0.005, So_sat]
# Kc, taui, delt, So_set, Kla_min, Kla_max, DKla_max, So_low, So_high, DO saturation
dt = DO_control_par[2]
#DO control setpoints
DO_setpoints = [0, 0, 2, 0, 2, 0, 0, 2]
kla0 = 0
class SbrEnv2(gym.Env):
"""custom Environment that follows gym interface"""
def run_step(self, batch_type, t, u, x_in, influent_mixed):
# Batch type assignment
# | This part was developed for assigning the batch type during time
# | It is not considered as the MVs which the DeepRL operated.
# | However, it should be altered as MV for next step.
# | 0: Filling phase | 1: Reaction phase | 2: Settling phase & 3: Draw phase & 4: idle phase
global So, Kla, kla, dcv, ie, e, t_t, x_t, Qw, eff_component
if (t_memory1[0] <= t) & (t < t_memory1[-1]):
batch_type = 0
elif (t_memory1[-1] <= t) & (t < t_memory2[-1]):
batch_type = 1
elif (t_memory2[-1] <= t) & (t < t_memory3[-1]):
batch_type = 1
elif (t_memory3[-1] <= t) & (t < t_memory4[-1]):
batch_type = 1
elif (t_memory4[-1] <= t) & (t < t_memory5[-1]):
batch_type = 1
else:
batch_type = 2 # Simplified, It includes Settling, Draw, and idle phases
# Actually it should be divided into each phase.
# Initialize
if t == 0:
# | initialize DO conc.
So.append(x_in[8])
# | initialize Kla
Kla.append(0) #kla
x_t = np.array(x0)
# Call: time range
# | Controller time interval: t_start ~ t_end(t_start + t_delta)
t_start = t # start time to run the SBR system (ODE)
t_end = t + t_delta # end time when the SBR running has finished (ODE)
# | derivative time interval: ~dt (t_delta/10)
t_range = np.linspace(t_start, t_end, (t_end - t_start) / dt)
# Call: Simulate the SBR system/each phases have been divided
# | 1. Filling phase
if batch_type == 0: # Filling phase?
x_out, t_range, Kla, So, dcv, ie, e = self.Sim_filling(
x_in, t_range, u, influent_mixed, Kla, So, dcv, ie, e)
Qw = 0
eff_component = []
# | 2. Reaction phase
if batch_type == 1: # reaction phase?
x_out, t_range, Kla, So, dcv, ie, e = self.Sim_rxn(
x_in, t_range, u, Kla, So, dcv, ie, e)
Qw = 0
eff_component = []
# | 3. Settling, drawing phases
# |
if batch_type == 2: # settling phase?
x_out1, t_range1, Qw, PE, SP, EQI, eff_component, So = self.Sim_Settling_Drawing(
x_in, t, t_ratio[5], t_ratio[6], t_delta, self.Qeff,
self.biomass_setpoint, So)
x_in = x_out1[-1]
x_out2, t_range2, Kla, So, dcv, ie, e = self.Sim_idle(
x_in, t_range1, t_ratio[7], u, Kla, So, dcv, ie, e)
x_out = np.vstack([x_out1, x_out2[1:]])
t_range1 = t_range1.tolist()
t_range2 = t_range2.tolist()
t_range = t_range1 + t_range2[1:]
t_range = np.array(t_range)
t_end = t_range[-1]
"""=========================================================================================
# | 3. Settling phase
if batch_type == 2: # settling phase?
self.sim_settling()
# | 4. Drawing phase
if batch_type == 3: # drawing phase?
self.sim_drawing()
# | 5. Idle phase
if batch_type == 4: # idle phase?
x_out = integrate.odeint(self.Idle_dxdt, x, t_range,
args=(self.Spar, self.Kpar, self.DO_control_par, Kla[-1],))
========================================================================================="""
# Update: update batch_type, time, and initial value for next step
# | 1. batch_type
# Simplified
# | 2. time
t = t_end
# | update the temporal trajectories
t_instant = t_range.tolist()
if t == 0:
t_t = t_t + t_instant
else:
t_t = t_t + t_instant[1:] # change np array to list
if t == 0:
x_t = np.array(x_out)
else:
x_t = np.vstack([x_t, x_out[1:]])
# | 3. initial value
# It will be updated in the section of STEP
reward = sbr_reward(DO_set(15), Kla, batch_type, Qin, Qw,
eff_component)
return batch_type, t, x_out, reward
1
5 (2.1) No Yes Yes No Rxn (AER)
1
6 (8.3) No No No No STL
2
7 (2.1) No No No Yes DRW
3
8 (6.3) No Yes No No IDL
4
(ref. Pons et al. Tbl. 1)
"""
DO_control_par = [
5.0, 0.00035, 0.02 / 24, 2, 0, 240, 12, 2, 5, 0.005,
DO_set(15)
]
# PID tuning
# | PID parameters: optimized parameters, (hand tuning)
Kc = 5
tauI = 0.00035
tauD = 0.005
class SbrEnv4(gym.Env):
"""custom Environment that follows gym interface"""
metadata = {'render.modes': ['human']}
def __init__(self):
# action space: Continuous Do setpoints | range-> -1~1 (applicable variation of DO, need to be checked with the other Envs.