""" IPA Pharmaceutical Roller Compactor Platform: Scale-Up & Performance Dataset v1.0 ================================================================================== Synthetic dataset modeling the IPA CL-series pharmaceutical roller compactor platform from R&D (CL25150) through full-scale production (CL100250), using IPA's published specifications and Johanson/Heckel physical models. Sources: - IPA Pharma RC Brochure (2026) - IPA Pharma Compactor Specifications page - IPA Roll Compactor page (industrial line dimensions) - IPA Products & Services PDF Key IPA differentiators modeled: - Twin feed screw design (HFS + VFS independent control) - Scalable platform: consistent ribbon quality across CL sizes - Integrated milling (PM-series in-air impact mills) - Proprietary PLC controls with internal control loops - Low bulk density powder processing capability - Efficient changeover and simplified maintenance THIS IS SYNTHETIC EDUCATIONAL DATA. NOT REAL CUSTOMER OR LAB DATA. """ import numpy as np import pandas as pd rng = np.random.default_rng(seed=2026) # ============================================================================= # IPA CL-SERIES PHARMA COMPACTOR SPECS (from published specifications) # ============================================================================= CL_MODELS = [ {"model": "CL25150", "roll_dia_in": 1.0, "roll_width_in": 6, "roll_dia_cm": 2.5, "roll_width_cm": 15, "max_pressure_lbs_in": 9900, "max_pressure_kn_cm": 17.5, "cap_light_lbs": (10, 23), "cap_heavy_lbs": (25, 56), "mill_size": None, "total_hp": 5, "total_kw": 3.75, "weight_lbs": 1200, "scale": "R&D / Lab"}, {"model": "CL30200", "roll_dia_in": 1.0, "roll_width_in": 8, "roll_dia_cm": 3.0, "roll_width_cm": 20, "max_pressure_lbs_in": 9900, "max_pressure_kn_cm": 17.5, "cap_light_lbs": (24, 54), "cap_heavy_lbs": (64, 140), "mill_size": "PM3", "total_hp": 2.75, "total_kw": 2.0, "weight_lbs": 2100, "scale": "Pilot"}, {"model": "CL50200", "roll_dia_in": 2.0, "roll_width_in": 8, "roll_dia_cm": 5.0, "roll_width_cm": 20, "max_pressure_lbs_in": 14800, "max_pressure_kn_cm": 26.0, "cap_light_lbs": (64, 140), "cap_heavy_lbs": (120, 265), "mill_size": "PM6", "total_hp": 12, "total_kw": 9.0, "weight_lbs": 5000, "scale": "Pilot / Small Production"}, {"model": "CL75200", "roll_dia_in": 3.0, "roll_width_in": 8, "roll_dia_cm": 7.5, "roll_width_cm": 20, "max_pressure_lbs_in": 8500, "max_pressure_kn_cm": 15.0, "cap_light_lbs": (95, 209), "cap_heavy_lbs": (182, 400), "mill_size": "PM6", "total_hp": 20, "total_kw": 15.0, "weight_lbs": 6000, "scale": "Production"}, {"model": "CL100250", "roll_dia_in": 4.0, "roll_width_in": 10, "roll_dia_cm": 10.0, "roll_width_cm": 25, "max_pressure_lbs_in": 9900, "max_pressure_kn_cm": 17.5, "cap_light_lbs": (200, 440), "cap_heavy_lbs": (425, 935), "mill_size": "PM8", "total_hp": 25, "total_kw": 19.0, "weight_lbs": 9500, "scale": "Full Production"}, ] # ============================================================================= # PHARMA MATERIALS (representative formulations) # ============================================================================= MATERIALS = { "MCC_PH101": { "label": "MCC PH-101 (Low Density Filler)", "feed_density_gcc": 0.32, "compact_density_gcc": 1.20, "heckel_k": 0.020, "heckel_a": 0.55, "deformation": "plastic", "flow_index": 4, "moisture_pct": 4.5, }, "lactose_DCL11": { "label": "Lactose DCL-11 (Direct Compression)", "feed_density_gcc": 0.62, "compact_density_gcc": 1.45, "heckel_k": 0.014, "heckel_a": 0.50, "deformation": "brittle", "flow_index": 7, "moisture_pct": 0.5, }, "mannitol_SD200": { "label": "Mannitol SD-200 (Spray Dried)", "feed_density_gcc": 0.48, "compact_density_gcc": 1.49, "heckel_k": 0.012, "heckel_a": 0.48, "deformation": "brittle", "flow_index": 6, "moisture_pct": 0.3, }, "API_blend_40pct": { "label": "API Blend 40% Drug Load", "feed_density_gcc": 0.38, "compact_density_gcc": 1.30, "heckel_k": 0.017, "heckel_a": 0.52, "deformation": "mixed", "flow_index": 3, "moisture_pct": 2.0, }, "vitamin_premix": { "label": "Vitamin/Mineral Premix (Nutraceutical)", "feed_density_gcc": 0.45, "compact_density_gcc": 1.35, "heckel_k": 0.015, "heckel_a": 0.50, "deformation": "mixed", "flow_index": 5, "moisture_pct": 3.0, }, } # ============================================================================= # PROCESS PARAMETERS # ============================================================================= ROLL_PRESSURE_FRACTIONS = [0.3, 0.5, 0.7, 0.85, 1.0] # fraction of max ROLL_SPEED_RPM = [2, 4, 6, 8, 10] HFS_SPEED_RPM = [15, 30, 50, 75, 100] # horizontal feed screw VFS_RATIO = [0.6, 0.8, 1.0, 1.2, 1.5] # VFS/HFS ratio N_REPLICATES = 3 # per condition # ============================================================================= # PHYSICS # ============================================================================= def compute_scf(pressure_lbs_in, roll_width_in): """Specific compaction force in kN/cm.""" return (pressure_lbs_in * 0.00444822) / (roll_width_in * 2.54) def ribbon_density(scf_kn_cm, roll_dia_cm, gap_mm, heckel_k, heckel_a, vfs_ratio, hfs_rpm, roll_rpm, deformation): """Compute ribbon relative density using Heckel + IPA twin-screw model.""" # Gap estimate based on roll geometry and pressure contact_len = np.sqrt(roll_dia_cm * 10 / 2 * 2.0 * gap_mm) pressure_mpa = (scf_kn_cm * 100) / max(contact_len, 3.0) # Heckel rd = 1.0 - np.exp(-(heckel_k * pressure_mpa + heckel_a)) # Twin feed screw VFS ratio effect — optimal around 1.0 vfs_optimality = np.exp(-((vfs_ratio - 1.0) ** 2) / (2 * 0.15 ** 2)) rd *= (0.90 + 0.10 * vfs_optimality) # Roll speed / dwell time if deformation == "plastic": rd *= (1.0 - 0.006 * max(roll_rpm - 4, 0)) elif deformation == "brittle": rd *= (1.0 - 0.001 * max(roll_rpm - 4, 0)) else: rd *= (1.0 - 0.003 * max(roll_rpm - 4, 0)) # HFS feed rate effect on pre-densification feed_ratio = hfs_rpm / max(roll_rpm, 1) feed_opt = np.exp(-((feed_ratio - 10) ** 2) / (2 * 5 ** 2)) rd *= (0.95 + 0.05 * feed_opt) return np.clip(rd, 0.35, 0.92) def compute_throughput(model, material, roll_rpm, hfs_rpm, rd): """Throughput in kg/hr based on capacity range and operating conditions.""" if material["feed_density_gcc"] <= 0.5: cap_range = model["cap_light_lbs"] else: cap_range = model["cap_heavy_lbs"] # Scale within capacity range based on operating conditions rpm_frac = (roll_rpm - 2) / 8 hfs_frac = (hfs_rpm - 15) / 85 operating_frac = 0.5 * rpm_frac + 0.5 * hfs_frac throughput_lbs = cap_range[0] + (cap_range[1] - cap_range[0]) * np.clip(operating_frac, 0, 1) throughput_kg = throughput_lbs * 0.4536 return throughput_lbs, throughput_kg def density_uniformity_cv(vfs_ratio, hfs_rpm, roll_rpm, model_scale): """Across-ribbon density CV%. Twin feed screw advantage.""" # Baseline: twin screw gives good uniformity base_cv = 2.5 # VFS ratio: optimal around 1.0 vfs_penalty = 2.0 * abs(vfs_ratio - 1.0) # Feed ratio ratio = hfs_rpm / max(roll_rpm, 1) ratio_penalty = 1.5 * abs(ratio - 10) / 10 # Scale: larger machines slightly harder to keep uniform scale_factors = {"R&D / Lab": 0, "Pilot": 0.2, "Pilot / Small Production": 0.3, "Production": 0.5, "Full Production": 0.7} scale_pen = scale_factors.get(model_scale, 0.3) return base_cv + vfs_penalty + ratio_penalty + scale_pen def compute_granule_yield(rd, fines_pct): """Yield = 100% - fines% - oversize%.""" oversize = max(0, 5 * (rd - 0.82)) # over-compacted ribbons resist milling return np.clip(100 - fines_pct - oversize, 40, 98) def compute_fines(rd, deformation): """Fines fraction after integrated PM-series mill.""" base = 55 * (1 - rd) if deformation == "brittle": base += 5 return np.clip(base, 3, 50) def compute_changeover_hr(model): """Changeover time — IPA advantage: efficient changeover design.""" # Scales with machine size base = 1.0 + 0.5 * np.log2(model["weight_lbs"] / 1200) return round(base, 1) # ============================================================================= # GENERATE DATASET # ============================================================================= rows = [] run_id = 0 for model in CL_MODELS: for mat_key, mat in MATERIALS.items(): for pf in ROLL_PRESSURE_FRACTIONS: for rs in ROLL_SPEED_RPM: for hfs in HFS_SPEED_RPM: for vr in VFS_RATIO: for rep in range(N_REPLICATES): run_id += 1 pressure = model["max_pressure_lbs_in"] * pf scf = compute_scf(pressure, model["roll_width_in"]) gap_mm = 1.5 + rng.uniform(-0.3, 0.3) rd = ribbon_density( scf, model["roll_dia_cm"], gap_mm, mat["heckel_k"], mat["heckel_a"], vr, hfs, rs, mat["deformation"]) rd += rng.normal(0, 0.010) rd = np.clip(rd, 0.35, 0.92) rib_density_gcc = rd * mat["compact_density_gcc"] porosity = 1 - rd cv = density_uniformity_cv(vr, hfs, rs, model["scale"]) cv += rng.normal(0, 0.3) cv = np.clip(cv, 1.0, 12.0) fines = compute_fines(rd, mat["deformation"]) fines += rng.normal(0, 1.5) fines = np.clip(fines, 2, 55) tp_lbs, tp_kg = compute_throughput(model, mat, rs, hfs, rd) tp_kg += rng.normal(0, tp_kg * 0.03) tp_kg = max(tp_kg, 1) tp_lbs = tp_kg / 0.4536 granule_yield = compute_granule_yield(rd, fines) granule_yield += rng.normal(0, 1.0) granule_yield = np.clip(granule_yield, 35, 99) zinchuk = "Yes" if 0.60 <= rd <= 0.80 else "No" changeover = compute_changeover_hr(model) # Specific energy power_kw = model["total_kw"] * (0.4 + 0.6 * pf) se = (power_kw / max(tp_kg, 1)) * 1000 # kWh/tonne rows.append({ "run_id": run_id, "compactor_model": model["model"], "scale": model["scale"], "roll_diameter_in": model["roll_dia_in"], "roll_width_in": model["roll_width_in"], "roll_diameter_cm": model["roll_dia_cm"], "roll_width_cm": model["roll_width_cm"], "max_roll_pressure_kn_cm": model["max_pressure_kn_cm"], "integrated_mill": model["mill_size"] if model["mill_size"] else "None", "total_power_kw": model["total_kw"], "material": mat_key, "feed_density_gcc": mat["feed_density_gcc"], "deformation_type": mat["deformation"], "roll_pressure_fraction": pf, "scf_kn_cm": round(scf, 2), "roll_speed_rpm": rs, "hfs_speed_rpm": hfs, "vfs_hfs_ratio": vr, "gap_width_mm": round(gap_mm, 2), "ribbon_rel_density": round(rd, 4), "ribbon_density_gcc": round(rib_density_gcc, 4), "ribbon_porosity": round(porosity, 4), "density_cv_pct": round(cv, 2), "fines_pct": round(fines, 2), "granule_yield_pct": round(granule_yield, 2), "in_zinchuk_window": zinchuk, "throughput_kg_hr": round(tp_kg, 1), "throughput_lbs_hr": round(tp_lbs, 1), "specific_energy_kwh_tonne": round(se, 2), "changeover_time_hr": changeover, "replicate": rep + 1, }) df = pd.DataFrame(rows) # Subsample to manageable size (full factorial is huge) # Keep ~3000 rows: stratified by model and material samples = [] for _, group in df.groupby(["compactor_model", "material"]): samples.append(group.sample(min(120, len(group)), random_state=42)) df_sampled = pd.concat(samples, ignore_index=True) df_sampled["run_id"] = range(1, len(df_sampled) + 1) df_sampled.to_csv("ipa_pharma_compactor_v1.0.csv", index=False) print(f"Wrote {len(df_sampled)} rows, {len(df_sampled.columns)} columns") print(f"\n=== Model distribution ===") print(df_sampled["compactor_model"].value_counts().sort_index()) print(f"\n=== Scale-up: mean throughput by model ===") print(df_sampled.groupby("compactor_model")["throughput_kg_hr"].mean().round(1)) print(f"\n=== Zinchuk compliance ===") print(df_sampled["in_zinchuk_window"].value_counts()) print(f"\n=== Mean density CV% by model ===") print(df_sampled.groupby("compactor_model")["density_cv_pct"].mean().round(2)) print(f"\n=== Mean granule yield by model ===") print(df_sampled.groupby("compactor_model")["granule_yield_pct"].mean().round(1))