Public Funding for Perceptive Engineering Limited

To develop machine learning techniques which will be embedded within Perceptive’s software offering, reducing the domain expertise level required by users to implement predictive maintenance systems. These tools will greatly increase process efficiencies and will open new markets.

This project seeks to integrate two complementary digital technologies within a continuous manufacturing environment. The fast start project brings together a UK SME (Perceptive Engineering), RTO (CPI, part of the High Value Manufacturing Catapult) and large corporate vendor (Pall Biotech) to incorporate a novel "digital wrapper" automation technology with Model Predictive Control & Prescriptive Maintenance system, creating a technology and scale independent intelligent control solution. As an initial application case study, this project will focus on applying the tools within the high value biopharmaceutical market. This global market is rapidly growing, and when this growth rate is combined with a compelling drive to move towards more intelligent manufacturing it becomes perfectly suited for focusing this game changing programme of work. Building on a wealth of expertise generated in previous IUK funded projects, the combination of the two digital technologies will greatly enhance the UK manufacturing capability in continuous manufacturing, and the output of a digital demonstrator hosted at a HVMC open access centre will ensure dissemination and rapid deployment across a breadth of sectors not limited to biological manufacturing.

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This project aims to advance digital precision aquaculture in China towards “Aquaculture 4.0”, which is a highly connected and automated cyber-physical system using digital technologies. Aquaculture production in China reached 45.5m ton in 2014 accounting for more than 60% of the global production. However, this sector in China suffers from the extremely low production efficiency, leading to high labour intensity, high consumpiton of energy and water, and severe enviroment pollution. Innovation is urgently needed for a transformation from traditional small-scale to digital industrialised aquaculture. The proposal is a timely response to the needs; it will apply and integrate the latest technologies of advanced sensors, 5G-based Internet of Things, Big Data analytics and automation to pilot highly digital precision aquaculture in China. The system is expected to deliver 10-fold increase in production efficiency. The project will open new business opportunities in China, and the developed solution will be also applicable to aquaculture worldwide.

Anaerobic Digesters (AD’s) aid the process by which organic matter is degraded to produce a gas mixture of methane and carbon dioxide by microorganisms. The performance of these digesters can be controlled by monitoring the variation in the parameters (e.g. pH, temperature, organic loading rate (OLR), and volatile fatty acids), which drive the process’ metabolic pathways, kinetics and microbial diversity. Any drastic changes in these can make it difficult to control the feedrate of the digester and adversely affect the biogas production. However there is a lack of robust statistical control strategies; the majority of existing automation systems, which provide a base layer of simple regulatory control, are based on standards from the 1970’s. Perceptive Engineering (PE) wants to address this by presenting real time information of the parameters in a simple intuitive format, using a process model that can provide optimal control of a small-scale AD.

This project aims to improve the reproducibility of consumer products across global supply chains, in the face of varying feedstock, changing recipes, sustainability targets and the needs of consumers. The challenge is to attain both the correct chemical composition and exact physical properties to satisfy the consumer, with the same ingredients giving vastly different properties due purely to processing. Concurrent design of process and product requires the properties to be related to the raw materials and the processing history. Using through batch data, Perceptive Engineering will develop a methodology and multivariate advisory system to visualise interactions between Raw Materials, Process and Product. These will assist with the scale up and transferability of processes; ensuring consistent quality.

This project aims to develop a batch 'Just in Time- Optimisation' demonstrator system to assess the feasibility of techniques that will improve efficiency and enable continual process imrovement of batch manufacturing processes, particularly in the SME market.

New reactor technologies are set to allow products that have traditionally been made in batches to be produced in a continuous manner. These reactors have the potential to transform manufacturing sectors by reducing energy, waste and the cost of manufacture and distribution. The technology will allow companies to use a single reactor for a number of products rather than investing in a number of task specific batch reactors. Therefore this project aims to develop an adaptive 'Dial a Product' control system to deliver the precise control required for these unique high value low volume manufacturing systems. Bringing together control design and analytical techniques to complement these reactors will enable the reactors to reach optimum performance quickly and efficiently as the manufacturer switches between products.