How can you optimize refinery processes using machine learning algorithms?

Some precautions must be taken before implementing artificial intelligence systems. Is my current process equipped with reliable and duly certified control systems? Do I already have a library or system operation history with operational maturity? Are those involved in the process properly trained and qualified to monitor the new learning system in the event of a breakdown?

Refineries can improve the quality of their operations, such as predicting equipment failures, energy efficiency improvement, process optimization and machine learning algorithms can analyze data collected from multiple sensors and historical records to identify patterns and anomalies , inter alia manufacturing plants will be made to make data-driven decisions that increase productivity and reduce downtime, these systems can adapt and learn from new data, and go have refined their models to further increase system efficiency over time.

Process Optimization is a very complex & exciting topic, but in a nutshell... ML algorithms must link the pieces of information in: Refinery process simulation software, refinery process modeling software, market & raw materials information, (pricing, availability, etc.), process control system, market & operation conditions and costs, energy consumption, safety (people, environment & equipment), maintenance needs and asset management software among others, to make adjustments to the linkages until the input data resembles the known outputs of said & desired data. With the ultimate purpose of maximizing OEE, productivity and therefore ROI.

Considero que los algoritmos de aprendizaje automático puede ayudar a optimizar varios procesos en las refinerías de las siguientes maneras: Predicción y control de procesos: Las IA se podrian usar para predecir variables clave como temperaturas, presiones, flujos en funcion al analisis de data y asi mantener las condiciones óptimas y maximizar el rendimiento del proceso Optimización en tiempo real: Se podria emplerar algoritmos de optimización basados en aprendizaje automático para determinar ajustes óptimos de variables operacionales que maximicen la producción, calidad. Estos sistemas adaptativos pueden reaccionar ágilmente a cambios en las condiciones del proceso

In chemical Prozesses are a lot of main/defined processes parameters, but also a lot of dirt parameters, like fouling, abrasion, viscosity, particles, which will change the results. If it happened within some hours, the experienced shift master will it notice. Some conditions will be happen exponential, so AI can detect minimal (non-risk) changes in very early times.

Um exemplo que já vivenciei ao longo da minha carreira foi a falta de padronização, antes de gerar dados é imprescindível padronizar a entrada do dado, somente com dados confiáveis conseguimos gerar números para tomada de decisão. Muito se fala atualmente em KPIs, na teoria parece simples, mas na pratica fazer a gestão da informação, criar padrões, treinar e doutrinar o gerador do dado é o processo mais desafiador dessa jornada de números e dados.

Precisamos lidar com uma abundância de dados todos os dias para manter as atividades. Dessa maneira, quanto mais qualidade, precisão e eficácia houver em uma análise e no processamento de dados, encontramos facilidade para, gerar relatórios relevantes, melhores previsões, trabalhar com estimativas estudando o comportamento dos consumidores e dos talentos.

Data preprocessing is crucial for the success of ML in optimizing refinery processes. The quality and quantity of data directly impact model accuracy and effectiveness. This entails collecting, cleaning, and preparing data for analysis and modeling, ensuring completeness, consistency, and relevance. Investing in robust data preprocessing techniques is essential for achieving reliable and meaningful results in refinery optimization.

Data handling involves acquiring, storing, processing, and managing data to extract meaningful insights. It includes tasks like data collection, cleaning, transformation, and analysis. Techniques such as data wrangling, visualization, and statistical analysis are employed to understand data patterns and trends. Data handling ensures data quality, security, and accessibility for decision-making and driving business outcomes. It's crucial in various fields like data science, business intelligence, and research.

Gather historical data on various parameters such as temperature, pressure, flow rates, etc. from the refinery process. Clean and preprocess the data to remove outliers and ensure consistency.

Predictive analysis using ML can anticipate equipment failures and process deviations before they occur, enhancing efficiency and preventing costly downtimes. By leveraging historical data and advanced algorithms, predictive analytics enables proactive decision-making and risk mitigation strategies. This proactive approach helps industries optimize maintenance schedules, improve productivity, and minimize disruptions, ultimately leading to significant cost savings and operational excellence.

With my experience, optimizing refinery processes with machine learning algorithms involves data analysis to identify patterns and anomalies, enhancing efficiency and predictive maintenance. Integrating AI models with real-time data streamlines operations and reduces downtime, leading to cost savings and improved performance.

1) Equipment Failure Prediction: ML models can analyze historical data from sensors and operational logs to identify patterns that precede equipment failures. By forecasting these failures, refineries can schedule maintenance during planned downtimes rather than reacting to unexpected breakdowns. 2) Reduction in Downtime: Early identification of potential failures allows for preemptive repairs or part replacements, minimizing unplanned downtime and maintaining continuous operations. 3) Cost Savings: Proactive maintenance reduces the costs associated with emergency repairs, spare parts inventory, and extended downtimes, leading to significant cost savings.

To me, the beauty of using ML as a predictive tool in industrial controls is that it never gets tired of analyzing every sensor input in relation to every other sensor. I usually explain it like this: An operator typically relies on high and low alarms to trigger a control action or a maintenance request. However, a good ML model will recognize that even though all sensors are within normal operating range (no alarm condition present), two or more sensors are changing in relation to each other in an unusual way. This causes the ML model to raise a caution flag effective suggesting to the operator "You should take a deeper look at these sensors and the processes they are related to."

Predictive analysis with machine learning involves training models on historical data to forecast future outcomes. It includes data collection, pre-processing, feature selection, model training, evaluation, and prediction. Applications span finance, marketing, healthcare, and more. Challenges include data quality, overfitting, and interpretability. Best practices involve domain knowledge, feature engineering, ensemble learning, model interpretability, and continuous monitoring.

Applying artificial intelligence on process control is most valuable when we use it for set point optimization. Hence most industrial controllers could have a great performance with a simple PID. When using machine learning for process optimization we should make sure that the training data has enough variance of operation scenarios. In the refinery where we could have a lot of variations on chemical and physical composition of raw materials the training data must be representative of this income variations or the model will not have enough robustness.

Process control with machine learning involves using algorithms to monitor and optimize industrial processes. It includes data collection, preprocessing, modeling, and control. ML models analyze real-time sensor data to detect anomalies, predict equipment failures, and optimize process parameters. Applications range from manufacturing to energy production. Challenges include data variability and model interpretability. Best practices involve domain expertise, model validation, and continuous improvement.

At the beginning the AI will optimize the stability of the required products from a known input educt. Later on, when the intelligence is high enough, the system can also do forecasts for new/unexpected educts. This seems important, when a refinery is not only using ex.g. crude oil from the northsea, but also quite different amounts from different pyrolysis/thermolysis plants with different Inputs. This will open the flexibility and therefore the stable delivery of required products.

Process control within a Shared Service Center, Machine Learning can enhance efficiency. ML algorithms analyze input data such as invoices, identify deviations, and automate real-time process adjustments. Through continuous learning, ML optimizes process control, ensuring consistency and minimizing manual interventions, leading to improved throughput and cost savings.

Controle de Qualidade: Utilização de ML para monitorar a qualidade dos produtos e ajustar parâmetros do processo em tempo real para manter os padrões desejados. Otimização de Parâmetros: Aplicação de algoritmos de otimização para ajustar continuamente os parâmetros do processo visando maximizar a eficiência e a produção. Sistemas de Controle Avançado: Integração de modelos de ML com sistemas de controle avançado (como DCS e SCADA) para automatizar ajustes de processo baseados em previsões e condições operacionais.

Natural gas combustion is typically the largest cost in cracking hydrocarbons, and controlling O2 trim is crucial for optimizing combustion efficiency. Most refineries control O2 within a "comfort zone," maintaining excess O2 to prevent hydrocarbon breakthrough. However, to operate in the "optimal zone," we must first ensure the quality and integrity of our O2 sensor data. The current standard for this measurement is a Tunable Diode Laser Spectroscopy (TDLS) instrument located directly above the burn box. Once we can trust our O2 measurement, we can use ML to maintain an O2 setpoint in the optimal range, which has the added benefit of reducing NOx emissions.

Modelagem de Consumo de Energia: Criação de modelos preditivos para analisar e prever o consumo de energia em diferentes partes da refinaria. Otimização de Energia: Desenvolvimento de estratégias para reduzir o consumo de energia, como o ajuste de processos e a implementação de práticas operacionais eficientes. Uso de Energias Renováveis: Integração de fontes de energia renovável e otimização do uso de energia a partir de fontes diversas.

Process efficiency with machine learning entails optimizing workflows and operations to maximize productivity and minimize waste. ML algorithms analyze data from various sources, identifying inefficiencies and opportunities for improvement. They automate repetitive tasks, predict equipment failures, and optimize resource allocation. Applications include manufacturing, logistics, and customer service. Challenges include data integration and model deployment. Best practices involve continuous monitoring, feedback loops, and collaboration between domain experts and data scientists.

Las tecnologias de control de equipos (PLC, VFD, Tarjetas de adquisicion de datos,LabView), permiten una interaccion casi completa de la inteligencia artificial en cada uno de los equipos utilizados en el proceso(control de velocidad en motores, controles de consumo de gas en quemadores, entre otros procesos) Por lo que la integracion de estos elementos a la IA, podria llegar a controlar automaticamente ciertas partes de los procesos, con el fin de conseguir una operacion mas eficiente

As technology progresses, we anticipate the emergence of more robust algorithms capable of tackling increasingly intricate tasks. The integration of ML with other emerging technologies like the Internet of Things and advanced analytics is poised to significantly enhance process optimization capabilities within refineries.

IA e IoT: Crescente integração da Internet das Coisas (IoT) com IA para permitir uma maior coleta de dados em tempo real e uma análise mais detalhada. Edge Computing: Uso de computação de borda para processar dados localmente em dispositivos próximos ao processo, reduzindo a latência e melhorando a velocidade de resposta. Sistemas Autônomos: Desenvolvimento de sistemas autônomos que podem tomar decisões operacionais sem intervenção humana, baseados em algoritmos de ML.

Implementation Strategy 1) Data Integration: Collect and integrate data from various sources, including sensors, control systems, and historical logs. 2) Model Development: Develop and train ML models using historical data to identify patterns and predict future events. 3) Real-time Monitoring: Implement real-time data monitoring and analysis to provide actionable insights and trigger automated responses. 4) Continuous Improvement: Regularly update and refine ML models based on new data and feedback to ensure ongoing optimization.

AI in chemical processes should learn first the safety limits, like the robot laws of Isaac Asimov. This should be the knowledge of all critical chemical reaction, espacially of suddenly (explosive) reactions!