Industrial Process Optimization

Have you ever tried to 'optimize' a machining operation based on 'machinability' data? How useful were these generic 'feeds and speeds'? One of the first lessons I learned as a young machinability consultant and engineer at TechSolve in Cinncinati OH was that optimal process paramters (tool material, geometry, coating, feeds, speeds, coolant, etc.) depend strongly on the specifics of a given operation, including workpiece material, geometry, and the cost structure of the specific job. Most importantly, I also quickly learned that the primary purpose of a machining process is to generate reliable and maximal profit. Therefore, an optimum process is one that is as robust and repeatable as possible, providing 'in spec' parts at the maximum profitability and throughput. The goal of machinability studies should be to generate necessary relationships and data, most importantly progressive tool-wear as a function of cutting time and the impact of tool-wear and feeds/speeds on product quality (dimensions, surface integrity, etc.). We need this information and its variability to model wear progression and the onset of unacceptable workpiece quality for data-driven process optimization. When optimizing, we are not simply trying to maximize metal removal rate and push tool-life to its maximum extent, but our optimization has to be constrained by the statistical variability of tool-wear and associated workpiece quality. While machinability standards such as ISO 8688-2:1989 or controlled/locked aerospace procedures suggest arbitrary end of tool-life criteria such as 0.3 mm maximum flank wear (~0.012"), the end-of-life criterion should always be intelligently defined based on workpiece quality; It does not matter that the tool can keep on cutting when we cannot sell the resulting workpiece and thus generate a profit. I have found that experienced machinists and engineers inherently know this and will consequently limit tool-life to relatively low values to avoid scrapping the workpiece. This practice makes a lot of sense, especially when detailed tool-wear and associated workpiece quality data are not available. Nevertheless, the benefits of even basic tool-wear analysis and quality-constrained process paramter optimization can be substantial. With relatively limited effort, profitability and throughput can often be improved anywhere from 20% for well estbalished (reasoanbly pre-optimized) processes and I have personally helped implement improvements as high as 20x greater process performance in particularly difficult-to-machine alloys and complex operations. The ROI for data-driven optimization depends on the cost metrics of each operation, but can be quite substantial in many cases. I personally feel that we should teach this advanced approach more broadly, particularly to experienced machinists and engineers, as well as the next generation of young professionals entering the field. Figure credit: https://lnkd.in/e5qQrtYM

𝗪𝗵𝘆 𝗘𝘃𝗲𝗿𝘆 𝗜𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿 𝗡𝗲𝗲𝗱𝘀 𝗣𝗠𝗧𝗦 𝗶𝗻 𝗧𝗵𝗲𝗶𝗿 𝗧𝗼𝗼𝗹𝗸𝗶𝘁 Precision and efficiency are non-negotiable in modern manufacturing. For industrial engineers, Predetermined Motion Time Systems (PMTS) are essential. PMTS provides a structured, data-driven approach to measure, analyze, and optimize workflows. It’s the ultimate tool for improving productivity and driving operational excellence. Here’s why PMTS is indispensable, explained through the TOOLS Framework: Time Standards, Optimization, Operations Clarity, Lean Practices, Sustainability. 𝟭. 𝗧𝗶𝗺𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀: 𝗠𝗲𝗮𝘀𝘂𝗿𝗲 𝘄𝗶𝘁𝗵 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 PMTS delivers accurate, repeatable time benchmarks. Set Standards: Define exact times for every task and motion. Remove Guesswork: Base planning on proven data, not assumptions. Enable Forecasting: Predict resource needs with confidence. Precise standards ensure reliable performance metrics. 𝟮. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: 𝗜𝗺𝗽𝗿𝗼𝘃𝗲 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 PMTS simplifies the process of identifying inefficiencies. Eliminate Waste: Remove non-value-added motions and tasks. Balance Workloads: Ensure tasks are evenly distributed among teams. Enhance Layouts: Design workstations for faster and smoother workflows. Optimization leads to higher productivity without extra costs. 𝟯. 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝗶𝘇𝗲 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗲𝘀 PMTS creates consistent workflows across teams and shifts. Develop SOPs: Build clear, actionable instructions for tasks. Streamline Communication: Ensure everyone follows the same process. Reduce Variability: Minimize errors and inconsistencies. Clarity builds confidence and ensures smooth operations. 𝟰. 𝗟𝗲𝗮𝗻 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀: 𝗗𝗿𝗶𝘃𝗲 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 PMTS is a cornerstone of lean manufacturing. Identify Bottlenecks: Use PMTS data to pinpoint process slowdowns. Support Kaizen: Continuously improve operations with precise data. Increase Value: Focus on tasks that directly impact the customer. Lean practices drive long-term cost savings and quality gains. 𝟱. 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆: 𝗕𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 PMTS supports sustainable operations by minimizing waste. Reduce Energy Use: Optimize workflows to save energy. Lower Material Waste: Improve process accuracy to prevent errors. Support Green Goals: Align operational improvements with sustainability initiatives. Sustainability and efficiency go hand in hand. 𝗧𝗵𝗲 𝗧𝗢𝗢𝗟𝗦 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 The TOOLS Framework shows why PMTS is essential for industrial engineers: Time Standards ensure precise planning. Optimization drives workflow efficiency. Operations Clarity creates consistency. Lean Practices improve productivity and value. Sustainability builds long-term success. PMTS isn’t just a tool—it’s a game-changer for modern industrial engineering. Ready to add PMTS to your toolkit?

Techno-Economic and Feasibility Study for the Optimization of the Ethanolamine Production Unit (Case Study: Qatar Petrochemical Company – 15,000 KTY) 💡 What’s this about? In this paper, I developed a full-process simulation of a large-scale ethanolamine production plant (MEA, DEA, TEA) based on one of the best licensors' industrial designs, using Aspen HYSYS V14 on a high-performance computing setup (i9 Gen 14th, 32 GB RAM). We analyzed, validated, and optimized the process technically and economically, delivering results that demonstrate both operational and financial viability. 📌 Key Achievements: ✅ 98.1% EO conversion ✅ 12.4% steam consumption reduction ✅ 9.1% cooling water savings ✅ MEA selectivity improved to 38% ✅ IRR: 21.5%, NPV: $72.4 million, Payback: 4.1 years 📎 Download the full paper here: 👉 https://lnkd.in/gtNgCQ88 #ProcessEngineering #AspenHYSYS #PetrochemicalDesign #TechnoEconomicAnalysis #Ethanolamine #QatarPetrochemical #ChemicalSimulation #MEA #DEA #TEA #SimulationOptimization #ResearchAndDevelopment #LinkedInResearch #EngineeringExcellence

🚀 Engineering Insight: What I Learned from Process Design Standards Today I explored some key principles from a Process Engineering Design Basis Manual, and it reminded me how much engineering decisions shape plant safety, reliability, and efficiency. Here are the most important takeaways: 🔹 1. Good Engineering Starts with Good P&IDs Pipe sizing, pressure-drop limits, minimum nozzle sizes, and flow-regime control are not small details — they define how safe and stable a process unit will operate. 🔹 2. Equipment Is Never Designed at 100% Load I learned how design margins protect real operations: • Heat exchangers: 10–20% extra duty • Pumps: 10–20% extra flow • Compressors/blowers: 10% margin These margins secure performance during fouling, aging, or unexpected process variations. 🔹 3. Safety Relief Philosophy Is Non-Negotiable Scenarios like cooling failure, blocked discharge, power loss, or tube rupture must be anticipated. Relief valves must comply with API 520/521/526, ensuring systems stay protected even during worst-case events. 🔹 4. Insulation & Tracing = Energy + Safety The manual highlights how insulation thickness is selected based on temperature ranges. Proper insulation reduces heat loss, prevents freezing, protects workers, and saves major energy costs. 🔹 5. Surge Volume & Level Philosophy Improves Plant Stability Surge time requirements like: • Feed to unit: 15–20 min • Feed to tower: 5–7 min • Furnace feed: 4–10 min help ensure the plant runs smoothly during disturbances and manual interventions. 🔹 6. Noise Engineering Protects Operators Designing equipment to maintain < 90 dBA in process units protects workers and meets industrial safety standards. Noise control is a real engineering discipline. 🔧 My Takeaway Engineering is much more than drawings and calculations — it’s a mindset of safety, optimization, and problem-solving. Every detail in a design basis document represents lessons learned from years of industrial experience. #ProcessEngineering #ChemicalEngineering #IndustrialEngineering #ProcessDesign #SafetyEngineering #EngineeringStandards #PlantOperations #EnergyEfficiency #EngineeringGrowth #LearningJourney

An RTO will negotiate with a penalty but it will not negotiate with physics. A common challenge when building Real-Time Optimization (RTO) models is translating plant physics into mathematical constraints. Both data scientists and process engineers want the same outcome: maximize throughput safely. But a subtle modeling shortcut can create problems. A Simple Example : Consider an RTO model optimizing flow through a transfer pump. From plant data we know the pump has a physical operating limit defined by its curve: Q ≤ Qmax Beyond this point the pump cannot maintain head and operation becomes unstable. However, during optimization this limit is sometimes implemented as a penalty term rather than a strict constraint: max⁡J= (Profit Rate) − (λ⋅max⁡(0,Q−Qmax)) This formulation allows the optimizer to violate the limit if the economics justify it. What the Algorithm Sees The optimizer evaluates a trade-off. Increasing the flow rate increases plant throughput by: +$8,000/hr The penalty for exceeding the pump limit is: −$3,000/hr Mathematically, the optimal solution becomes: Q > Qmax The algorithm is doing exactly what it was designed to do. What the Plant Sees : The pump curve is not negotiable. Once the optimizer demands flow beyond the stable operating region: • Pump head collapses • Cavitation begins • Flow oscillations appear • The control system trips to protect the equipment The optimization result was economically optimal in the model, but physically infeasible in the plant. The Practical Lesson In industrial optimization models, constraints must be translated carefully. Hard constraints : g(x)≤0 • pump curves • heat exchanger duty • compressor surge limits • safety interlocks These define the physical feasible region. Soft constraints : J = (Profit) − (λ⋅penalty) • quality buffers • operational preferences • economic trade-offs These guide how the optimizer behaves inside that region. When plant physics defines the boundary and optimization explores within it, RTO becomes powerful. If not, the optimizer may simply find the most profitable way to violate the laws of the equipment. #Industry40 #ProcessEngineering #DataScience #RealTimeOptimization #IndustrialAI #OTITConvergence #Manufacturing #Advancedprocesscontrol

#Note2Self 📍In metallurgy & plant operations, everyone talks about stability, optimization, consistency, and performance improvement, but very few can mathematically measure, track, diagnose, and optimize the system. Most people stay in the realm of concepts and buzzwords instead of numbers and proofs. ☆Why this happens? Metallurgy as taught traditionally focuses on: ▪︎metallurgy fundamentals ▪︎process diagrams ▪︎rules of thumb ▪︎empirical heuristics But modern metallurgical excellence requires a second toolkit — the quantitative science toolbox: 🏷Skill 》Why it matters in metallurgy ▪︎Calculus 》Rate processes, gradients, sensitivity, optimization ▪︎Statistics》Confidence in plant changes, hypothesis testing, SPC ▪︎Probability》Uncertainty in ore feed, risk in decision making ▪︎Linear algebra 》Multivariate balances, matrix mass balancing, PLS ▪︎Optimization 》Cyanide control, grind optimization, reagent strategy ▪︎Machine learning》Predictive metallurgical control & soft sensors Most metallurgists can describe a problem… Very few can model, test, quantify, and optimize it. ☆Why operators plateau without math? Without math, troubleshooting becomes: ▪︎emotional ▪︎reaction-based ▪︎anecdotal ▪︎reactive instead of proactive ▪︎“tweak & hope” method Instead of: predict → test → measure → optimize → sustain They get trapped in “looks stable / plant feels ok / recovery seems fine”. ☆The difference? 🏷Normal metallurgist says 》Advanced metallurgist does “We need stable flotation”》Calculate variance, Cpk, control limits “Carbon activity dropped”》Time-series trend + ANOVA + regression “Upsets cause gold loss”》Dynamic modelling + derivative response “Add more lime”》Optimization model w/ cost & constraint equations “Process noisy today”》Statistical filtering & predictive models “Balance is off”》Least squares matrix reconciliation ☆Why math = power in metallurgy? Because the plant is a nonlinear, multivariate dynamic system So to control it, you need to quantify: ▪︎Inputs → Outputs ▪︎Variance → Response ▪︎Cause → Effect ▪︎Noise → Signal ▪︎Cost → Recovery gain ▪︎Risk → Confidence No amount of talking replaces: ▪︎regression coefficients ▪︎partial derivatives ▪︎mass balance matrices ▪︎SPC charts ▪︎feature importance from ML ▪︎objective function gradients ☆In short Most metallurgists operate with qualitative language in a quantitative world. They talk stability. They don’t calculate stability. They talk optimization. They don’t solve optimization problems. They talk process control. They don’t build predictive models or evaluate uncertainty. That’s the difference between a “plant metallurgist” and a “data-driven process optimizer”. Start moving into the rare category that can not just say: “We improved recovery” but confidently state: “Recovery increased 1.8% ± 0.3 at 95% confidence with reagent cost reduction of $0.45/t, supported by regression coefficients, sensitivity analysis, and validated prediction error bounds.” That's elite.

Multi-objective formulation optimization, too few samples, dismal model performance. Where to go? If you've worked on industrial formulations, you've seen this before: a handful of experiments, properties that fight each other, and models that look great on training data… only to be seriously overfit on noise. A new paper in Chemical Science offers a surprisingly practical account, using multi-objective optimization of self-healing polyurethanes as a concrete case. Rather than hiding the failures, the authors walk through them and turn them into a playbook that you can adapt: Step 1. Start with a random baseline A small, randomly sampled dataset is used to train standard models and then naively expanded with more random experiments. Overfitting dominates, making it clear that random sampling doesn't solve the problem. Step 2. Diagnose failure instead of tuning harder Feature-importance analysis shows that chemically important variables contribute little to predictions, confirming that the models are learning spurious correlations rather than structure–property relationships. Step 3. Redefine the inputs using chemistry-informed descriptors Raw formulation ratios are replaced by a small set of descriptors encoding stoichiometric balance, chain-extender balance, and hard/soft segment ratio. This reduces the experimental design space while encoding known chemical mechanisms. Step 4. Design the dataset instead of sampling blindly A gradient-designed dataset is constructed in descriptor space. With just 9 designed samples, model generalization improves substantially, showing that data quality and coverage matter more than sample count. Step 5. Use Pareto optimization and expand the design space Multi-objective optimization makes trade-offs visible. When progress stalls, key descriptor ranges are widened to explore new regions. Step 6. Consolidate datasets and validate predictions Complementary designed datasets are merged to predict candidates beyond the current Pareto front. But initial experimental validation fails dramatically, signaling extrapolation beyond the covered chemical space. Step 7. Fill gaps, re-optimize, and validate successfully Failures are traced to missing regions of descriptor space. Targeted experiments fill these gaps, after which re-optimization yields predictions that closely match experiments. In total, ~20 samples prove sufficient for this system. Step 8. Confirm physical consistency, convergence, and generalization Structure–property analysis aligns with established polymer physics, further data no longer improves the Pareto front, and the same workflow generalizes on a different polyurethane system. If you're stuck in complex formulation modeling challenges, this paper is worth a careful read. 📄 Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes, Chemical Science, December 23, 2025 🔗 https://lnkd.in/eTAg7QkW

What happens when you aim industrial AI at production scheduling but treat it like every other engineering problem? We built a multi-agent AI system that achieved a 21% increase in profit. Here’s how: 1. Make the goals explicit Production scheduling is a complex process with numerous trade-offs. Highest demand or most efficient run? Overtime or on-time delivery? We spelled out the real goals and KPIs so the agent system knew exactly which knot it had to untangle. 2. Capture expertise through machine teaching Machine teaching breaks the job into bite-size skills. An engineer shows the system why a decision works, not just what happened in the data. Rather than rely purely on data, machine teaching transfers deep human expertise into the system - digitizing decades of experience and knowledge, crucial as expert operators retire. 3. Structuring the Multi-Agent System The multi-agent system was designed to mimic human decision-making: Sensors: Gather real-time data on production status, resources, and external market conditions. Skills: Modular units responsible for specific actions, such as forecasting demand, optimizing scheduling, or adapting to sudden changes. Each skill can evolve on its own, giving the plant the same modular flexibility you expect from any well-engineered system. 4. Establishing a Performance Benchmark Good engineering demands clear benchmarks. We ran a standard optimization-based system as our baseline. This allowed us to objectively measure whether our AI agents delivered measurable improvements. 5. Rigorous Testing & Iteration Engineering thrives on iteration. We created and tested 13 agent system designs, continuously iterating based on performance data. Each iteration leveraged insights from the previous, systematically improving performance until we identified the optimal solution. --- By treating AI as an engineered system (modular, explainable, and configurable) it demonstrates significant potential results: ✅ 21% higher profit margins ✅ Improved adaptability to rapidly changing market conditions ✅ Preservation and amplification of valuable human expertise Full breakdown of the build and tests is below.👇 #ProductionScheduling #IndustrialAI #MachineTeaching #SmartManufacturing

The convergence of AI techniques and GPU-accelerated optimization is solving time sensitive industrial problems in seconds. By combining real-time data platforms like Databricks with powerful solvers like NVIDIA cuOpt, enterprises are moving beyond static spreadsheets to dynamic, resilient execution. 🚚 For Logistics: This means solving massive Vehicle Routing Problems (VRP) instantly. Fleets can dynamically re-route thousands of vehicles based on real-time traffic and weather, slashing fuel costs and hitting precise delivery windows. 🏭 For Manufacturing: The same math applies to the factory floor. By feeding constrained demand forecasts directly into the optimization engine, production schedules align machine uptime and labor shifts with market needs the moment they change. The result is a more agile, responsive enterprise where planning keeps pace with the real world.

Yesterday, I gave a presentation on the evolution, current state, and future of Machine Learning for Process Optimization in Microfabrication. Microfabrication is one of the most complex manufacturing processes, a chain of hundreds of tightly coupled, high precision steps. Even the smallest variation can impact yield dramatically. That’s where ML shines. Some key takeaways from my talk: 🔹 Bayesian Optimization helps tune process recipes (temperature, pressure, gas flow, time) using far fewer experiments. 🔹 Reinforcement Learning enables adaptive control, learning by doing to improve process stability. 🔹 Virtual Metrology predicts critical dimensions and film thickness from live sensor data, cutting wait times and variability. 🔹 Deep Learning models (like DeepSEM-Net and DTWAN) detect wafer defects and predict yield with high accuracy. 🔹 Predictive maintenance models now spot equipment drift before it leads to breakdowns, improving uptime. As fabs evolve toward self optimizing systems, the combination of physics informed ML, explainable AI, and robust data pipelines is redefining what’s possible. Would love to hear from others working at the intersection of semiconductors and machine learning, about what innovations are they the most excited about? #MachineLearning #Semiconductors #Microfabrication #ProcessOptimization #AIinManufacturing #BayesianOptimization #ReinforcementLearning #VirtualMetrology