Process Design

Process design is the systematic approach to developing chemical processes from initial concept to detailed engineering. It integrates all aspects of chemical engineering to create safe, efficient, and economically viable processes.

Process Design Methodology

Design Hierarchy

1. Conceptual design: Process synthesis and alternative evaluation
2. Preliminary design: Equipment sizing and cost estimation
3. Detailed design: Complete specifications and drawings
4. Construction and commissioning: Implementation and startup

Process Synthesis

Developing the process flow structure:

• Raw material selection
• Reaction pathway analysis
• Separation system design
• Energy integration
• Waste minimization

Process Flow Diagrams (PFDs)

PFD Components

• Major equipment: Reactors, columns, heat exchangers
• Process streams: Flow rates, compositions, conditions
• Utilities: Steam, cooling water, electricity
• Control systems: Instrumentation and control loops

Piping and Instrumentation Diagrams (P&IDs)

Detailed diagrams showing:

• Piping specifications: Sizes, materials, insulation
• Instrumentation: Sensors, controllers, valves
• Safety systems: Relief devices, interlocks

Equipment Sizing and Specification

Reactor Design

• Type selection: Batch, CSTR, PFR
• Volume calculation: Based on reaction kinetics
• Heat transfer requirements: Jackets, coils
• Material selection: Corrosion resistance

Separation Equipment

• Distillation columns: Tray or packing selection
• Heat exchangers: Area calculation, type selection
• Pumps and compressors: Head requirements, power

Storage and Handling

• Tank sizing: Working volume, residence time
• Piping design: Pressure drop, material selection
• Safety systems: Relief valves, containment

Economic Analysis

Capital Cost Estimation

• Equipment costs: From vendors or correlations
• Installation factors: Piping, instrumentation, buildings
• Contingency: For uncertainties and changes

Operating Cost Estimation

• Raw materials: Consumption rates and prices
• Utilities: Energy, water, steam requirements
• Labor: Operating and maintenance staff
• Maintenance: Routine and turnaround costs

Economic Evaluation

• Net Present Value (NPV): Time value of money
• Internal Rate of Return (IRR): Project profitability
• Payback period: Time to recover investment
• Break-even analysis: Minimum production for profitability

Safety and Environmental Considerations

Hazard Analysis

• HAZOP: Hazard and Operability Studies
• LOPA: Layer of Protection Analysis
• QRA: Quantitative Risk Assessment

Environmental Impact

• Emissions control: Air, water, solid waste
• Resource efficiency: Energy and water minimization
• Sustainability: Life cycle assessment

Advanced Process Design Concepts

Process Integration and Intensification

Pinch Analysis:

• Composite curves: Hot and cold stream integration
• Grand composite curve: Utility targeting
• Heat exchanger network synthesis: Minimum energy requirement

Mathematical Formulation: For heat exchanger network optimization:

Subject to energy balance and temperature constraints.

Process Intensification:

• Equipment integration: Combined unit operations
• Novel equipment: Microreactors, rotating packed beds
• Alternative energy sources: Microwave, ultrasound
• Enhanced transport: Supercritical fluids, ionic liquids

Advanced Optimization Techniques

Mathematical Programming:

• Linear Programming (LP): For linear objective functions
• Nonlinear Programming (NLP): For nonlinear models
• Mixed-Integer Programming (MIP): For discrete decisions
• Stochastic Programming: For uncertainty handling

Multi-objective Optimization: Pareto-optimal solutions considering:

• Economic performance
• Environmental impact
• Safety considerations
• Operational flexibility

Genetic Algorithms and Metaheuristics: For complex, non-convex optimization problems with multiple local optima.

Risk Analysis and Decision Making

Monte Carlo Simulation: For uncertainty propagation in economic analysis:

Where cash flows $CF_t$ follow probability distributions.

Real Options Analysis: Valuing flexibility in project design:

• Option to expand: Future capacity increases
• Option to abandon: Early termination
• Option to switch: Multiple product capabilities

Decision Analysis:

• Decision trees: Sequential decision making
• Influence diagrams: Complex decision networks
• Value of information: Cost-benefit of additional data

Advanced Economic Analysis

Detailed Capital Cost Estimation:

• Factorial method: Equipment cost multiplied by factors
• Detailed take-off: Component-level cost estimation
• Modular costing: Pre-fabricated unit costs

Operating Cost Breakdown:

• Direct costs: Raw materials, utilities, labor
• Indirect costs: Overhead, administration, R&D
• Capital-related costs: Depreciation, interest, insurance

Advanced Financial Metrics:

• Discounted cash flow rate of return (DCFROR)
• Modified internal rate of return (MIRR)
• Economic value added (EVA)
• Return on capital employed (ROCE)

Sustainability and Green Engineering

Life Cycle Assessment (LCA):

• Goal and scope definition: System boundaries
• Life cycle inventory: Material and energy flows
• Impact assessment: Environmental impacts
• Interpretation: Results and improvement analysis

Green Chemistry Principles:

• Atom economy and E-factor
• Renewable feedstocks
• Energy efficiency
• Waste prevention

Circular Economy:

• Material recycling: Closed-loop systems
• Energy recovery: Waste-to-energy
• Product stewardship: Extended producer responsibility

Advanced Safety and Risk Management

Quantitative Risk Assessment (QRA):

• Individual risk: Risk to specific individuals
• Societal risk: F-N curves for population exposure
• Risk contours: Geographic risk distribution

Inherently Safer Design:

• Intensification: Smaller inventories
• Substitution: Less hazardous materials
• Attenuation: Less severe conditions
• Simplification: Fewer failure points

Safety Instrumented Systems (SIS):

• Safety integrity levels (SIL): Performance requirements
• Layers of protection analysis (LOPA): Independent protection layers
• Fault tree analysis (FTA): System failure probability

Dynamic Process Simulation

Dynamic Modeling:

• Ordinary differential equations (ODEs): Lumped parameter systems
• Partial differential equations (PDEs): Distributed parameter systems
• Algebraic-differential equations (DAEs): Combined systems

Process Control Integration:

• Dynamic optimization: Optimal control trajectories
• Startup and shutdown sequences: Transient operation
• Disturbance rejection: Robust operation under variability

Digital Twins:

• Real-time simulation: Mirror of physical process
• Predictive maintenance: Failure prediction
• Operator training: Virtual environment

Advanced Process Synthesis

Superstructure Optimization: Mathematical formulation for process alternatives:

Subject to:

Heat Integration: Simultaneous heat exchanger network synthesis:

• Stream splitting: For better heat recovery
• Multiple utilities: Different temperature levels
• Capital-energy tradeoff: Optimal network complexity

Water Integration:

• Water pinch analysis: Minimum freshwater usage
• Regeneration recycling: Treatment and reuse
• Zero liquid discharge: Complete water recovery

Project Management and Implementation

Stage-Gate Process:

• Concept screening: Initial feasibility
• Preliminary design: Technical and economic assessment
• Detailed design: Complete engineering
• Construction: Physical implementation
• Commissioning: Startup and operation

Earned Value Management (EVM):

• Planned value (PV): Budgeted work
• Earned value (EV): Completed work value
• Actual cost (AC): Actual expenditure
• Cost and schedule performance indices

Value Engineering: Systematic approach to optimize life cycle costs while maintaining required functions and performance.

Real-World Application: Advanced Ethylene Plant Design with Optimization

Designing and optimizing a world-scale ethylene production facility using advanced techniques:

Advanced Process Overview

• Feedstock flexibility: Ethane, propane, naphtha, gas oil
• Advanced cracking: High-severity furnaces with COT > 850°C
• Product slate optimization: Maximize ethylene or propylene based on market
• Energy integration: Cogeneration with gas turbines
• Scale: 1,500,000 tonnes/year with expansion capability

Advanced Design Decisions and Optimization

Furnace Optimization:

• Coil design: Millisecond vs. short residence time
• Feedstock selection: Economic optimization based on prices
• Operating severity: Temperature-residence time trade-off
• Decoking strategy: Online vs. offline decoking

Separation Sequence Optimization: Using mixed-integer nonlinear programming (MINLP):

Subject to:

• Product purity constraints
• Energy balance equations
• Equipment capacity limits
• Logical constraints for sequence selection

Heat Integration:

• Pinch analysis: Minimum approach temperature optimization
• Heat pump integration: For low-temperature distillation
• Cogeneration: Gas turbine with waste heat recovery
• Advanced heat exchanger networks: Plate-fin, spiral types

Advanced Economic Analysis with Risk Assessment

import numpy as np
import scipy.stats as stats

# Advanced capital cost estimation with uncertainty
def estimate_capital_cost(capacity, feedstock_type, location, technology_level):
    """Estimate capital cost with uncertainty using Monte Carlo simulation"""
    
    # Base cost factors ($/tonne)
    base_costs = {
        'ethane': 1200,
        'propane': 1300,
        'naphtha': 1500,
        'gas_oil': 1700
    }
    
    # Location factors
    location_factors = {
        'USGC': 1.0,
        'MiddleEast': 0.9,
        'Asia': 1.1,
        'Europe': 1.2
    }
    
    # Technology factors
    tech_factors = {
        'conventional': 1.0,
        'advanced': 1.15,
        'state_of_art': 1.3
    }
    
    base_cost = base_costs[feedstock_type]
    total_factor = location_factors[location] * tech_factors[technology_level]
    
    # Uncertainty: ±20% with triangular distribution
    uncertainty = np.random.triangular(0.8, 1.0, 1.2, 1000)
    
    capital_costs = capacity * base_cost * total_factor * uncertainty
    
    return capital_costs

# Monte Carlo simulation for project economics
def monte_carlo_economics(capacity, feedstock, product_prices, operating_costs, n_simulations=10000):
    """Perform Monte Carlo simulation for project economics"""
    
    # Generate random samples
    capital_costs = estimate_capital_cost(capacity, feedstock, 'USGC', 'advanced')
    price_variation = np.random.normal(1.0, 0.2, n_simulations)  # 20% price volatility
    operating_cost_variation = np.random.normal(1.0, 0.1, n_simulations)  # 10% cost volatility
    
    # Calculate NPV for each simulation
    npv_results = []
    for i in range(n_simulations):
        # Simple 10-year cash flow calculation
        revenue = capacity * product_prices['ethylene'] * price_variation[i]
        op_cost = operating_costs * operating_cost_variation[i]
        annual_cash_flow = revenue - op_cost
        
        # Discounted cash flow (10% discount rate)
        npv = -capital_costs[i] + np.sum([annual_cash_flow / (1.1)**t for t in range(1, 11)])
        npv_results.append(npv)
    
    return npv_results

# TODO: Perform advanced economic analysis with risk assessment
# Steps:
# 1. Define input parameters with distributions
# 2. Run Monte Carlo simulation
# 3. Analyze results and calculate risk metrics

# Input parameters
plant_capacity = 1500000  # tonnes/year
feedstock_type = 'ethane'
product_prices = {'ethylene': 800, 'propylene': 900, 'butadiene': 1200}  # $/tonne
base_operating_cost = 300  # $/tonne

# Run Monte Carlo simulation
npv_distribution = monte_carlo_economics(plant_capacity, feedstock_type, product_prices, base_operating_cost)

# Calculate risk metrics
mean_npv = np.mean(npv_distribution)
std_npv = np.std(npv_distribution)
prob_positive_npv = np.mean(np.array(npv_distribution) > 0)
var_95 = np.percentile(npv_distribution, 5)  # Value at Risk at 95% confidence

print("Advanced Economic Analysis with Risk Assessment:")
print(f"Mean NPV: ${mean_npv:,.0f}")
print(f"Standard deviation: ${std_npv:,.0f}")
print(f"Probability of positive NPV: {prob_positive_npv:.1%}")
print(f"Value at Risk (95%): ${var_95:,.0f}")

# Real options analysis for expansion capability
def real_options_valuation(base_npv, expansion_cost, expansion_probability, expansion_multiplier):
    """Calculate value of expansion option"""
    expansion_value = max(0, base_npv * expansion_multiplier - expansion_cost)
    option_value = expansion_probability * expansion_value
    return option_value

expansion_option_value = real_options_valuation(mean_npv, 200000000, 0.6, 1.3)
print(f"\nReal Options Analysis:")
print(f"Expansion option value: ${expansion_option_value:,.0f}")
print(f"Total project value (NPV + option): ${mean_npv + expansion_option_value:,.0f}")

# Sensitivity analysis
def sensitivity_analysis(base_case, variations):
    """Perform sensitivity analysis on key parameters"""
    sensitivities = {}
    for param, variation in variations.items():
        new_npv = base_case * variation
        sensitivity = (new_npv - base_case) / base_case
        sensitivities[param] = sensitivity
    return sensitivities

sensitivity_variations = {
    'ethylene_price': 1.1,  # +10%
    'operating_cost': 1.1,  # +10%
    'capital_cost': 1.1,    # +10%
    'capacity': 1.1         # +10%
}

sensitivities = sensitivity_analysis(mean_npv, sensitivity_variations)
print(f"\nSensitivity Analysis (% change in NPV per 10% parameter change):")
for param, sens in sensitivities.items():
    print(f"  {param}: {sens:.1%}")

Your Challenge: Integrated Process Design Project

In this exercise, you'll design a complete chemical process from raw materials to final product, including economic evaluation.

Goal: Design a methanol production process and evaluate its economic viability.

Process Description

Produce methanol from natural gas via steam reforming:

Reactions:

1. Steam reforming: $CH_4 + H_2O \rightarrow CO + 3H_2$
2. Water-gas shift: $CO + H_2O \rightarrow CO_2 + H_2$
3. Methanol synthesis: $CO + 2H_2 \rightarrow CH_3OH$

Design Basis:

• Production: 100,000 tonnes/year methanol
• Operating hours: 8,000 hours/year
• Natural gas price: $3/MMBtu
• Methanol price: $400/tonne

Process Steps

1. Feed preparation: Natural gas desulfurization
2. Reforming: Steam methane reformer
3. Compression: Synthesis gas compression
4. Methanol synthesis: Reactor and separation
5. Purification: Distillation to product specification

# Process design parameters
production_rate = 100000  # tonnes/year
operating_hours = 8000    # hours/year
natural_gas_price = 3     # $/MMBtu
methanol_price = 400      # $/tonne

# Stoichiometric requirements (simplified)
# CH4 + H2O -> CH3OH (overall stoichiometry)
methane_requirement = 0.75  # tonnes CH4/tonne MeOH

# TODO: Calculate key design and economic parameters
# Steps:
# 1. Calculate hourly production rate
# 2. Calculate natural gas consumption and cost
# 3. Estimate capital and operating costs
# 4. Calculate economic indicators

hourly_production = 0
annual_methane_required = 0
annual_gas_cost = 0

# Cost estimation factors
capital_cost_per_tonne = 1000  # $/tonne capacity
fixed_operating_cost = 0.10    # fraction of capital cost/year
variable_operating_cost = 50   # $/tonne (excluding raw materials)

total_capital_cost = 0
annual_fixed_cost = 0
annual_variable_cost = 0
total_annual_cost = 0

annual_revenue = 0
annual_profit = 0
simple_payback = 0

print("Process Design Summary:")
print(f"Hourly production: {hourly_production:.1f} tonnes/h")
print(f"Annual natural gas cost: ${annual_gas_cost:,.0f}")
print(f"Total capital cost: ${total_capital_cost:,.0f}")
print(f"Annual operating cost: ${total_annual_cost:,.0f}")
print(f"Annual revenue: ${annual_revenue:,.0f}")
print(f"Annual profit: ${annual_profit:,.0f}")
print(f"Simple payback: {simple_payback:.1f} years")

# Economic viability assessment
if simple_payback <= 5:
    print("Project is economically attractive")
else:
    print("Project may require optimization for better economics")

What process improvements could enhance profitability? How would changes in natural gas prices affect project economics?