Chemical Plant Control Across Edge and Cloud Processing Layers

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Solution Overview

Problem

Chemical plants face challenges in efficiently leveraging data for production efficiency due to restrictive safety standards, which hinder the migration of embedded control systems to cloud computing systems.

Innovation Solution

A distributed computing system with multiple deployment layers, including a first processing layer for asset-level monitoring, a second processing layer for plant-level contextualization, and an external processing layer for enhanced computing resources, allows for efficient data handling and application orchestration.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If cloud computing system is used for data processing, then productivity and analytics capability are improved, but safety standards and system reliability deteriorate due to restrictive access requirements

Engineering Contradiction:

Improveproduction efficiencyVSAvoidsafety standard compliance

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system is divided into multiple deployment layers (edge layer, cloud layer, hybrid layer) that can be independently configured. Each layer processes data according to safety requirements, with the edge layer handling critical real-time data locally while the cloud layer processes non-critical data, thus maintaining safety standards while enabling cloud computing benefits.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

A distributed computing system acts as an intermediary between the chemical plant's control systems and cloud computing resources. This intermediary layer filters, processes, and manages data flow according to safety protocols, allowing cloud computing capabilities to be utilized without directly compromising system safety or reliability.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Adaptability or versatility

If embedded control systems are migrated to cloud computing, then adaptability and computing resources are improved, but latency and system availability worsen

Engineering Contradiction:

Improvecomputing flexibilityVSAvoidresponse latency

Core Design Contradiction:

Adaptability or versatilityVSSpeed

Solution Approach 1:

The system adds a spatial dimension to computing by distributing processing across multiple locations and layers (edge devices, local servers, cloud data centers). This multi-dimensional architecture allows critical functions to run locally with low latency while non-critical functions utilize remote cloud resources, thus achieving both adaptability and speed requirements.

Inventive Principle:

Principle #17Another dimension (Dimensionality change)

Solution Approach 2:

Different parts of the system are assigned different computing qualities based on their requirements. Critical control functions are executed locally on edge devices with deterministic response times, while non-critical analytics and reporting functions are executed in the cloud with higher flexibility, thus optimizing both latency and adaptability for different system functions.

Inventive Principle:

Principle #3Local quality

3Loss of information

If mass data is transferred to external systems, then analytics capability is improved, but data security and access control worsen

Engineering Contradiction:

Improvedata utilizationVSAvoiddata security risk

Core Design Contradiction:

Loss of informationVSObject-affected harmful factors

Solution Approach 1:

Only the necessary and non-critical data elements are extracted and transferred to cloud systems for analytics, while critical process control data remains within the secure plant boundaries. This selective extraction approach enables data utilization benefits while minimizing security exposure by removing only what is essential from the secure environment.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system changes the parameters of data transfer by implementing different data classification levels, encryption methods, and access control policies for different data types. Critical data is transferred with strict security parameters (encryption, authentication), while non-critical data can be transferred with relaxed parameters, thus enabling data utilization while managing security risks through parameter optimization.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS12298753B2Method for monitoring and/or controlling one or more chemical plant(s)

Publication Date: 2025.05.13 BASF SE

AI summary

Disclosed is a method for monitoring and/or controlling a chemical plant (12) with multiple assets via a distributed computing system (10) with more than two deployment layers (14, 16, 30, 32, 34), wherein the deployment layers (14, 16, 30, 32, 34) comprise at least two of a first processing layer (14), a second processing layer (16, 32, 34) and an external processing layer (30), the method comprising the steps of: providing (60) a containerized application (48, 50) including an asset or plant template specifying input data, output data and an asset or plant model, deploying (62) the containerized application (48, 50) to execute on at least one of the deployment layers (14, 16, 30, 32, 34), wherein the deployment layer (14, 16, 30, 32, 34) is assigned based on the input data, a load indicator, or a system layer tag, and executing the containerized application (46, 52, 54) on the assigned deployment layer(s) (14, 16, 30, 32, 34) to generate output data for controlling and/or monitoring the chemical plant (12), providing (66) the generated output data for controlling and/or monitoring the chemical plant (12).