BACKGROUND: Computational and data-intensive technologies such as model predictive control and artificial intelligence have the potential to increase process yields in the process industry. In modular plant designs, the flexible and difficult-to-predict use of a particular module presents the challenge of providing the right amount of data, computing power, and storage capacity to use these technologies in each module.OBJECTIVE: Simplifying the deployment and reconfiguration of compute-intensive applications for modular plants.METHODS: Reviewing the current state of the art and combining the modular plant concept with a cloud-based Control-as-a-Service (CaaS) approach.RESULTS: A cloud-based, containerized CaaS concept that supports the deployment compute- and data-intensive methods for modular plants. The complementing communication stack consisting of standards such as APL, TSN and OPC UA FX.CONCLUSION: The proposed architecture simplifies IT/OT integration, eases the deployment of compute-intensive technologies, and allows for GMP compliant pre-qualification of software modules. However, the limiting factors like plant safety, conformity to regulations and widespread architecture adoption are not covered in detail and will be subject of future research.

Modular Automation offers promising technologies for the process and chemical industry. It meets the requirements for greater flexibility and a shorter development cycle for production facilities by dividing the process into smaller standardized units. Key element of Modular Automation is the Module Type Package (MTP). It represents a standardized and manufacturer-independent description of the automation interface for self-contained production units, or Process Equipment Assemblies (PEA), and endows the plug-and-produce capability of PEAs. Software solutions to program MTP compatible PEAs are mostly provided by the Programmable Logic Controller (PLC) manufacturers. They are proprietary and bound to certain hardware and even programming languages. Therefore, their suitability for implementation of soft sensors based on data-driven advanced analytics is strongly limited. In this article we present an opensource Python package, MTPPy, designed for rapid prototyping of MTP-capable soft sensors with a focus on AI-based research applications. MTPPy is aimed at the accelerated deployment of soft sensors in the modular plants; thus, closing the gap between the data-driven model development and integration into the production.

With the advent of the fourth industrial revolution i.e. Industry 4.0, plants and factories are becoming smarter and interconnected. The transitions demand vertical integration and seamless connectivity. For this purpose, there is a need for semantic communication between various devices including the heavily resource-constrained field devices. To address this, a real-time capable hardware-based implementation of a well-established semantic communication protocol, i.e. OPC Unified Architecture was designed and developed. This chip-based implementation is power-efficient and compact, making it suitable for the field level. The chip was analyzed and incorporated in a demonstrator as a proof of concept of its integration at field level in a plant module of the process industry. Various opportunities are also examined where the chip could be utilized to deliver benefits to existing and future technologies.

The introduction of AI methods in production or production-related environments meets with resistance from operators due to their lack of relevant experience and their responsibility for plant safety. To overcome these inhibitions one requires prototypical implementations, which offer considerable benefits, meet the highest requirements for reliability, are accepted by the operating personnel, and get support from those in charge. As a result, AI technologies must be embedded into the complex IT/OT infrastructure of the companies. Traceability, maintainability and longevity must also be guaranteed. As a first step towards this, we present a concept of the demonstrator and its first results, which should make AI comprehensible by visualizing challenges and exploring possibilities in the process industry.

This paper considers an implementation of the predictive maintenance for rotating equipment in a chemical process plant by means of the NAMUR Open Architecture (NOA). Various methods and challenges for monitoring of rotating equipment are described and compared. An approach based on the motor current measurement for the use case at a BASF plant was selected due to construction limitations. The focus is on connectivity aspects as well as details of implementation of the NOA concept. Use of the NOA concept made it possible to deploy the developed monitoring system into a real plant within one day during a planned maintenance window without any permanent alterations to the existing plant. The monitoring system works in parallel and affects neither the process nor the process control directly. Discussion of the first data gathered by the installed monitoring system showed that additional context information about the process are of great importance for the comprehensive analysis. At the end of the paper, improvements with regards to the connectivity and integration of additional sensors as well as further activities such as coupling of context information about the process and development of a decision making system are discussed.