Consulting Services
- Process Performance improvement
- Yield, conversion, Productivity, Utility Consumption
- New Equipment Design
- Debottleneck Equipment Performance
Services
How it works
One configurable engine for your process data, and the engineering services built on top of it.
What we do
Service offerings
Process Modeling and Simulation
- Process Modeling for Performance improvement
- AI-ML based Modeling for Performance improvement
Customized Software Development
- Digital Twins
- Soft Sensor Development
- Golden Batch analysis
- Process Simulator
- Process Assistant Development
DashBoard and ML Integration
- Process Performance improvement
- Yield, conversion, Productivity, Utility Consumption
- New Equipment Design
- Debottleneck Equipment Performance
Platform
One engine, your process.
Variable names, units, batch definitions and model choices are configured per industry, so the same workflow serves fermentation, chemicals and other batch or continuous processes.
Typical inputs
- Historian exports (CSV / Excel)
- Batch records with start and end markers
- Offline lab assays to train against
- Process knowledge from your engineers, built into the setup
Ferment IQ
Your fermentation plant, live, on one screen.
Ferment IQ runs inside your plant network and turns live process data into a clear operational picture: where every batch is, which variables are moving, and which batches need attention now.
Batch progress at a glance
Every active batch with its phase, elapsed time and key variables, laid out the way your shift team reads a plant.
Deviations, early
Compare live trajectories against reference behaviour so drifting batches surface before they become lost batches.
Runs on-premise
Deployed inside the plant network, next to your data. Operational data stays where it belongs.
Service
Process Control and Optimization
What is process control?
Process control is a vital aspect of industrial operations that involves the use of technology to monitor and regulate various processes within chemical industries. It ensures that manufacturing processes operate efficiently, consistently, and safely by maintaining the desired output within specific limits. Whether it's controlling temperature, pressure, flow rates, or chemical composition, process control systems enable industries to produce high-quality products while minimizing waste and energy consumption.
An early use of feedback control for manufacturing purposes was by Watt to regulate steam turbines in the late 18th century, followed by servomechanisms for ship steering in the 1920s.
Why process control?
In the chemical industry, precise control over processes is crucial for several reasons:
- Quality Assurance: Ensures that the final product meets the desired specifications, reducing the likelihood of defects and improving customer satisfaction.
- Safety: Maintains safe operating conditions, preventing accidents and equipment failures that could lead to hazardous situations.
- Efficiency: Optimizes the use of resources such as raw materials, energy, and time, leading to cost savings and enhanced productivity.
- Environmental Compliance: Helps in adhering to environmental regulations by controlling emissions and reducing waste, contributing to sustainable operations.
- Innovation and Competitiveness: Facilitates the integration of advanced technologies such as AI, machine learning, and predictive analytics, allowing companies to stay competitive and innovate in a rapidly evolving industry.
Our capabilities
At RPDS, our capabilities in process control are rooted in a deep understanding of both classical and modern control strategies. We excel in implementing advanced process control systems that enhance efficiency, stability, and safety across various industries. Our expertise includes real-time monitoring, process simulation, and control loop optimization, ensuring your operations run smoothly and respond dynamically to changing conditions. By leveraging cutting-edge technologies and data-driven insights, we provide customized solutions that drive performance and reliability in every aspect of your process control systems.
Process Simulation and Modeling
Create accurate models of your processes to predict outcomes and improve control strategies, using different modeling approaches such as first-principles and data-based models.
PID Tuning and Control Loop Optimization
Fine-tune PID controllers, both model-based and online, and optimize control loops for enhanced process stability.
Model Predictive Control
Apply dynamic process models to predict future behavior and optimize control actions, improving efficiency and handling complex systems with multiple variables and constraints.
Service
Soft Sensor Development
What is a soft sensor?
In process industries, the variables that determine profitability are often the most difficult to measure continuously. Product composition, reaction conversion, biomass concentration, moisture content, and quality indices typically depend on laboratory analysis or expensive online analyzers that introduce cost, maintenance burden, and delayed feedback.
A soft sensor, also known as a virtual sensor or inferential model, addresses this limitation by estimating an unmeasured process variable using existing plant measurements and process relationships.
Using inputs such as temperatures, pressures, flow rates, compositions, and historical operating data, soft sensors generate real-time estimates of critical quality variables directly within plant operations.
Rather than replacing instrumentation, soft sensors extend measurement capability beyond physical sensing limits.
Use of soft sensors
Soft sensors are deployed where conventional measurement falls short. Variables such as product composition, conversion, biomass concentration, and quality indices are often difficult or expensive to measure continuously. Soft sensors bridge this gap by converting readily available process data into real-time estimates, enabling improved control, reduced variability, and enhanced operational efficiency.
Typical applications include:
- Distillation
- Product composition and purity prediction
- Reactors
- Conversion, yield, and reaction endpoint estimation
- Polymerization
- Melt flow index, viscosity, and molecular property prediction
- Bioprocessing
- Biomass, substrate, and product concentration estimation
- Utilities and treatment
- Energy efficiency, COD/BOD, and process performance monitoring
Soft sensors are increasingly integrated into APC, MPC, and real-time optimization environments to improve control performance and reduce process variability.
How to develop a soft sensor
Developing an industrial soft sensor requires a structured engineering workflow:
- Define the target variable. Select a variable that directly impacts quality, throughput, energy, or process economics.
- Collect and prepare data. Gather historical process and laboratory data while accounting for operating conditions, delays, and data quality.
- Select relevant inputs. Choose variables based on process understanding and causal relationships rather than statistical correlation alone.
- Build the model. Develop inferential models using approaches such as machine learning or first-principles models.
- Validate and deploy. Evaluate performance across operating regimes and integrate the model into plant systems with ongoing monitoring.
Advantages of soft sensors
Soft sensors deliver value across operational and economic dimensions:
- Continuous visibility into previously unmeasured variables
- Reduced dependence on laboratory analysis
- Lower analyzer capital and maintenance costs
- Faster response to process deviations
- Improved quality consistency
- Enhanced APC and closed-loop control performance
- Better utilization of existing process data
Key point
The greatest benefit is not measurement replacement; it is improved decision-making speed.
Where the RPDS tool helps
Developing soft sensors for industrial processes traditionally requires a combination of process expertise, data science knowledge, and significant development effort. RPDS AI simplifies this process by providing a guided, no-code workflow that enables process engineers and domain experts to build soft sensor models using existing plant data.
Users can upload historical process data, explore and visualize operating conditions, perform data preprocessing, identify key process variables, and develop predictive models through an intuitive interface. The platform streamlines the model development workflow, reducing the complexity typically associated with machine learning projects.
Whether estimating product quality, reaction conversion, biomass concentration, moisture content, yield, or other difficult-to-measure process variables, RPDS AI helps organizations transform plant data into actionable process intelligence.
By reducing dependence on specialized programming skills and accelerating model development, RPDS AI enables chemical, pharmaceutical, biotech, food processing, and manufacturing companies to unlock greater value from their operational data and support data-driven process improvement initiatives.
Key benefits
- No coding or machine learning expertise required
- Guided workflow for soft sensor model development
- Built specifically for industrial process data
- Data visualization and exploratory analysis tools
- Integrated preprocessing and feature selection capabilities
- Multiple machine learning model options for evaluation
- Faster model development and validation
Service
Predictive Maintenance
The history of predictive maintenance in the chemical industry reflects the evolution of technology and data analytics. Traditionally, maintenance in industrial settings, including chemical plants, followed a reactive or preventive model. Equipment was either repaired after a failure occurred or maintained on a fixed schedule, regardless of its actual condition. This approach often led to unnecessary downtime, increased costs, and suboptimal asset performance.
The advent of computing power and data storage in the late 20th century paved the way for predictive maintenance. Initially, simple rule-based systems were used to monitor equipment conditions and trigger alerts when predefined thresholds were crossed. With the rise of artificial intelligence and machine learning in the 21st century, predictive maintenance truly came into its own: advanced algorithms could now analyze vast datasets, identifying complex patterns and correlations beyond the reach of traditional methods.
In the chemical industry, this progression has led to AI-driven predictive maintenance systems. These systems use historical data, sensor inputs, and real-time analytics to predict equipment failures and maintenance needs with a high degree of accuracy, and they keep improving as the models are refined.
Predictive maintenance workflow
Algorithm development starts with data that describes your system in a range of healthy and faulty conditions. The raw data is preprocessed into a form from which you can extract condition indicators: features that help distinguish healthy conditions from faulty ones. You can then use the extracted features to train a machine learning model that can:
- Detect anomalies
- Classify different types of faults
- Estimate the remaining useful life (RUL) of your machine
Finally, you deploy the algorithm and integrate it into your systems for machine monitoring and maintenance.
Service
Fault Diagnosis
AI expedites bottleneck identification through data analysis, enhancing process efficiency.
Try it yourself
The point is not a single model. The point is a workflow your engineers can rerun, inspect and trust.
Open the RPDS Tool on the penicillin dataset and run the cleaning, soft-sensor and golden-batch steps yourself.