Revise bsm1 for metab #1597
Revise bsm1 for metab #1597
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…o BSM1; only low flow rates cannot solve; need to increase initial flowrate by at least 2 orders of mag
…instead of 10/hr (can't solve at 10). Additionally, neither model is numerically stable; next, try remove P1 pump
…with and without metab
| # solve_stat = {} | ||
| # clones = {} | ||
| # for i, (k,v) in enumerate(new_comps.items()): | ||
| # clone_name = f"clone_change_{k}" | ||
| # m_clone = m.clone() | ||
| # old_val = pyo.value(m_clone.fs.feed.conc_mass_comp[0,k]) | ||
| # m_clone.fs.feed.conc_mass_comp[0,k].fix(v) | ||
| # print(f"{k} was fixed.") | ||
| # res = solver.solve(m_clone, tee=True) | ||
| # if pyo.check_optimal_termination(res): | ||
| # solve_stat[k]=1 | ||
| # else: | ||
| # solve_stat[k]=0 | ||
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| # clones[clone_name] = m_clone | ||
| # m_clone.fs.feed.conc_mass_comp[0,k].fix(old_val) | ||
| # del m_clone |
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@Morgan88888888 This bit here is for troubleshooting which component concentration might be problematic in terms of model convergence; it checks the converge by fixing concentration values one at a time and independently.
Uncomment this to (1) clone model, (2) fix concentration to new value for that one component, (3) attempt to solve that cloned model and store optimal termination as 1 and failure as 0 in solve_stat, (4) store model clone in clones dict for optional diagnostics later, (5) delete clone and restart, cycling to the next component to probe
Just seeing now that the steps where I record original default value and refix the original default value are not necessary anymore since I moved to just cloning the model in each iteration.
| for i, (k, v) in enumerate(new_comps.items()): | ||
| print(f"{k} was fixed.") | ||
| m.fs.feed.conc_mass_comp[0, k].fix(v) |
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After ensuring that I can solve the model with each component's newly fixed concentration separately, I proceed to solve the model with ALL new concentration values fixed simultaneously to ensure that the model solves without issue.
| m.fs.feed.alkalinity.fix(47.7 * pyo.units.mol / pyo.units.m**3) | ||
| m.fs.feed.temperature.fix(308) |
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Being cautious about this, I then fix the new alkalinity and temp values to see if those cause issues.
| if "conc_mass_comp" in var.name: | ||
| print(var.name) | ||
| iscale.set_scaling_factor(var, 10 / (pyo.value(var) + 1e-6)) |
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scaling concentration is important
| m.fs.R3.KLa.fix(20) | ||
| m.fs.R4.KLa.fix(20) | ||
| m.fs.R5.KLa.fix(20) |
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problems solving when Kla at 10
| # m.fs.feed.flow_vol.fix(0.00011 * 1e4 | ||
| # # 134500221723699 | ||
| # ) | ||
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| # for var in m.fs.component_data_objects(pyo.Var, descend_into=True): | ||
| # if "flow_vol" in var.name: | ||
| # iscale.set_scaling_factor(var, 1* pyo.value(var)) | ||
| # m.fs.R1.volume.fix(1000 *5e-4 * pyo.units.m**3) | ||
| # m.fs.R2.volume.fix(1000 *5e-4 * pyo.units.m**3) | ||
| # m.fs.R3.volume.fix(1333 *5e-4* pyo.units.m**3) | ||
| # m.fs.R4.volume.fix(1333 *5e-4* pyo.units.m**3) | ||
| # m.fs.R5.volume.fix(1333 *5e-4* pyo.units.m**3) | ||
| # for i in range(5): | ||
| # blk = getattr(m.fs,f"R{i+1}") | ||
| # iscale.set_scaling_factor(blk.volume, 1e2) | ||
| # iscale.calculate_scaling_factors(m) |
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Tried lower influent flowrates but could not solve at 0.001 or 0.0001 (0.01 and higher did converge however). I also tried adjusting reactor volumes accordingly, with no success.
| COD_removal_w_metab = pyo.value( | ||
| 1 - m.fs.Treated.properties[0].COD / m.fs.feed.properties[0].COD | ||
| ) |
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example recording COD removal with metab. Another line does so without metab. We can do this for other metrics too, but COD is likely most critical.
| brew_comp_vals = [ | ||
| 0.02, | ||
| 5.6, | ||
| 0.025, | ||
| 1.156, | ||
| 1.00e-10, | ||
| 1.00e-10, | ||
| 1.00e-10, | ||
| 1.00e-10, | ||
| 1.00e-10, | ||
| 0.140067, | ||
| 0.06, | ||
| 0.05324, | ||
| ] | ||
| m.fs.feed.alkalinity.fix(40.0 * pyo.units.mol / pyo.units.m**3) | ||
| m.fs.feed.temperature.fix(308) | ||
| for var in m.fs.component_data_objects(pyo.Var, descend_into=True): | ||
| # if "flow_vol" in var.name: | ||
| # iscale.set_scaling_factor(var, 1e1) | ||
| # if "temperature" in var.name: | ||
| # iscale.set_scaling_factor(var, 1e-1) | ||
| # if "pressure" in var.name: | ||
| # iscale.set_scaling_factor(var, 1e-3) | ||
| if "conc_mass_comp" in var.name: | ||
| # if pyo.value(var) <= 1e-10: | ||
| # iscale.set_scaling_factor(var, 1) | ||
| # else: | ||
| iscale.set_scaling_factor(var, 1e-3 / (pyo.value(var) + 1e-10)) | ||
| iscale.calculate_scaling_factors(m.fs) | ||
| brew_comps = {comps[i]: brew_comp_vals[i] for i in range(len(comps))} | ||
| solve_stat = {} | ||
| clones = {} | ||
| for i, (k, v) in enumerate(brew_comps.items()): | ||
| clone_name = f"clone_change_{k}" | ||
| m_clone = m.clone() | ||
| old_val = pyo.value(m_clone.fs.feed.conc_mass_comp[0, k]) | ||
| m_clone.fs.feed.conc_mass_comp[0, k].fix(v) | ||
| res = solver.solve(m_clone, tee=True) | ||
| if pyo.check_optimal_termination(res): | ||
| solve_stat[k] = 1 | ||
| else: | ||
| solve_stat[k] = 0 | ||
| print(f"{k} was fixed.") | ||
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| clones[clone_name] = m_clone | ||
| m_clone.fs.feed.conc_mass_comp[0, k].fix(old_val) | ||
| del m_clone | ||
| print(solve_stat) | ||
| m.fs.R3.KLa.fix(20) | ||
| m.fs.R4.KLa.fix(20) | ||
| m.fs.R5.KLa.fix(20) | ||
| for i, (k, v) in enumerate(brew_comps.items()): | ||
| print(f"{k} was fixed.") | ||
| m.fs.feed.conc_mass_comp[0, k].fix(v) | ||
| # for var in m.fs.component_data_objects(pyo.Var, descend_into=True): | ||
| # # if "flow_vol" in var.name: | ||
| # # iscale.set_scaling_factor(var, 1e1) | ||
| # # if "temperature" in var.name: | ||
| # # iscale.set_scaling_factor(var, 1e-1) | ||
| # # if "pressure" in var.name: | ||
| # # iscale.set_scaling_factor(var, 1e-3) | ||
| # if "conc_mass_comp" in var.name: | ||
| # # if pyo.value(var) <= 1e-10: | ||
| # # iscale.set_scaling_factor(var, 1) | ||
| # # else: | ||
| # iscale.set_scaling_factor(var, 1e-1/(pyo.value(var)+1e-10)) | ||
| # iscale.calculate_scaling_factors(m.fs) | ||
| res = solver.solve(m, tee=True) | ||
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| from idaes.core.util.model_diagnostics import DiagnosticsToolbox | ||
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| dt = DiagnosticsToolbox(m) | ||
| if pyo.check_optimal_termination(res): | ||
| print("Success!") | ||
| else: | ||
| raise RuntimeError("Failed to solve") | ||
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| COD_removal_no_metab = pyo.value( | ||
| 1 - m.fs.Treated.properties[0].COD / m.fs.feed.properties[0].COD | ||
| ) |
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without metab (using brewery compositions translated to ASM1)
Fixes/Resolves:
NA
Summary/Motivation:
Aid with model convergence when connecting METAB to activated sludge process.
Changes proposed in this PR:
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