AI-Enabled Autonomous Optimization for Continuous Manufacturing

Continuous Quality Improvement is Our #1 Goal

ProcessMiner’s predictive system solves problems before they happen, by making recommendations and prescribing solutions in real-time. In doing so, ProcessMiner™ minimizes raw material usage and reduces other costs, while maintaining both speed and product quality.

Modeling and learning

Turn-Key, AutoPilot Solution

Easily and quickly deployed turn-key solution for manufacturing processes allows manufacturers to do analysis without a staff of data scientists.

Data Assessment

Advanced Data Science

We pair data science, technology and industry expertise to unleash the full potential of digital manufacturing: complete autonomous control for continuous quality improvement.

Monitoring and Prediction

Real-time Anomaly Detection

Real-time artificial intelligence machine monitoring, predictions, prescriptions and recommendations, gives manufacturers access to metrics both off- and online.

AI Optimization in manufacturing

Closed-loop Control

Predictive to root cause automatic vision control. Our AutoPilot platform achieves unprecedented autonomous, closed-loop control of machines for continuous manufacturing.

How the ProcessMiner™ AutoPilot Platform Works

ProcessMiner™ is a decision automation platform that uses real-time artificial intelligence to help reduce energy consumption, raw materials, chemical usage and cost.

Manufacturing + ProcessMiner = Sustainability Goals

Manufacturing Operational Improvement

  • Address Skilled Labor Shortage
  • Improve Product Quality
  • Increase Yield
  • Increase Throughput

    Manufacturing Sustainability Improvement

    • Save Energy
    • Reduce Emissions
    • Reduce Chemicals Usage
    • Save Raw Materials
    • Decrease Water Usage

    Monetize Your Data

    Step #1: Collect Clean, Relevant and Actionable Data

    The first step in the optimization process is to access the historical data kept on the server and analyze it for data quality and consistency. This analysis will determine if the data collected is clean and consistently formatted for the purpose of optimizing the manufacturing process.

    Partner and Affiliate Organizations

    Autonomous Manufacturing Made Simple


    Monitoring historical data ensures you’re collecting clean, relevant, trustworthy and actionable data to relay back to the server.


    Machine-learning system begins predicting performance measures and identifying improvement opportunities.

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    Easy to read suggestions for minimizing variability though improvement, preventative and corrective actions.

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    Closed Loop

    Through our predictive machine learning and artificial intelligence systems, real-time recommendations are automatically sent for autonomous manufacturing.


    Continual improvement that optimizes process performance and variability. The longer it’s used, the more ProcessMiner improves.


    With good, clean data in hand, applies advanced data science and machine learning techniques to your manufacturing process to find insights and impact ROI.

    What We Do

    Continuous Quality Improvement

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    AI-enabled autonomous optimization for continuous manufacturing

    Reduce energy consumption, raw materials and chemicals usage

    Build a real-time AI platform that is self adaptive & evolving

    Meet and exceed quality specs through reducing variability

    Research & design to solve manufacturing problems and improve quality

    Case Studies

    LSTM Autoencoder for Extreme Rare Event Classification in Keras

    LSTM Autoencoder for Extreme Rare Event Classification in Keras

    Here we will learn the details of data preparation for LSTM models, and build an LSTM Autoencoder for rare-event classification. This post is a continuation of my previous post-Extreme Rare Event Classification using Autoencoders. In the previous post, we talked about the challenges in an extremely rare event data with less than 1% positively labeled data.

    Extreme Rare Event Classification using Autoencoders in Keras

    Extreme Rare Event Classification using Autoencoders in Keras

    In a rare-event problem, we have an unbalanced dataset. Meaning, we have fewer positively labeled samples than negative. In a typical rare-event problem, the positively labeled data are around 5–10% of the total. In an extremely rare event problem, we have less than 1% positively labeled data.

    Estimating Non-Linear Correlation in R

    Estimating Non-Linear Correlation in R

    Correlation estimations are commonly used in various data mining applications. In my experience, nonlinear correlations are quite common in various processes. Due to this, nonlinear models, such as SVM, are employed for regression, classification, etc.

    Solenis has been a strategic partner of ProcessMiner since 2017. Their AI and ML autonomous solution for manufacturing has been instrumental in supporting our efforts to assist our customers on their digital journey. The solutions we have developed with ProcessMiner enable our customers to make practical decisions each day about their operation that improve productivity, reduce variability, and improve sustainability as we optimize their application of our chemistries. We believe ProcessMiner’s technology is a game-changer for the industries we serve, and we are proud to be working with them to bring this technology to more customers around the world.

    Accelerate Your Manufacturing Efficiency

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