Perceptive Engineering Ltd.
Perceptive Engineering Ltd.
Agency: GTR | Branch: EPSRC | Program: | Phase: Training Grant | Award Amount: 4.35M | Year: 2012
This proposal is to establish a Doctoral Training Centre embedded within the EPSRC Centre for Innovative Manufacturing in Continuous Manufacturing and Crystallisation. The Centre tackles a core issue in the manufacture of fine chemicals and pharmaceuticals - an important sector for the UK - and has strong support from industry including major companies from the Pharma sector (GSK, AstraZeneca, Novartis). We will enable manufacturers to shift their production processes from traditional batch methods, which can be expensive, inefficient and limited in their control, to continuous methods that offer solutions to each of these issues. The Centre can potentially make a huge impact on the UKs manufacturing efficiency in a £multi-billion sector. Although the EPSRC Centre does have a limited cohort of PhD students at the moment, there is no provision for 2012 onwards. As the largest of the current EPSRC Centres, achieving a critical mass of researchers across the core disciplines is a key goal as we establish a world class research activity. It is also important for our industry partners that the UK can meet their needs for trained people in this area and embed continuous processing in their manufacturing plants. We will establish a unique and tailored training and research programme that meets these needs. The proposed DTC will add an extra dimension to the EPSRC Centre, training 3 cohorts of PhD students with the skills, knowledge and understanding to help meet the challenges of continuous manufacturing. Recruiting 45 students over 3 intakes in 2012/13/14 the DTC will mark a step change in activity in this field. We will attract the very best PGR students and equip them to become future leaders who will be influential in implementing this transformational change. The research will contribute to opportunites for new products that can be brought more quickly to market, using more reliable, energy-efficient and profitable manufacturing routes. The Centre inolves a multidisciplinary team across 7 universities who will contribute to the DTC including expertise in pharmaceutical sciences, chemical engineering, chemistry, operations management and manufacturing. Thus, the embedded DTC will provide students with a unique programme of training across disciplines, using a combination of modules and research activities. . Students will register in a host institution and will follow a 1+3 year model. Year 1 will comprise intensive formal training delivered in 10 residential courses across the universities, including transferable skills and group project work, allowing the cohort to gain identity and build team spirit and fellowship. Elective specialist elements will then develop knowledge in preparation for PhD research, along with exploratory cross-disciplinary mini-projects. Assessment of modules and projects will be by a combination of presentations and reports. Years 2-4 will focus on multidisciplinary, co-supervised PhD research projects, allowing the student to work with academics from across the Centre. Further transferable skills training and cohort building activities will include an annual two-week Summer School, and networking opportunities with other cohorts. The proposed DTC has captured the imagination of our industrial collaborators with 5 additional companies having added their support to the creation of this DTC. In addition to substantial cash contributions they are offering training, site visits, project input, mentoring and short-term industrial placements. We will create a national community of highly skilled researchers in continuous manufacturing and crystallisation, building the scale and quality of research to enhance the international reputation of our Centre and make a real difference to the manufacture of high-value products, such as pharmaceuticals. The training of 45 high quality DTC PhD students will make a major contribution towards this goal.
Agency: GTR | Branch: Innovate UK | Program: | Phase: Collaborative Research & Development | Award Amount: 265.73K | Year: 2013
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.
PubMed | University of Surrey and Perceptive Engineering Ltd.
Type: | Journal: Bioresource technology | Year: 2016
This paper presents a spreadsheet calculator to estimate biogas production and the operational revenue and costs for UK-based farm-fed anaerobic digesters. There exist sophisticated biogas production models in published literature, but the application of these in farm-fed anaerobic digesters is often impractical. This is due to the limited measuring devices, financial constraints, and the operators being non-experts in anaerobic digestion. The proposed biogas production model is designed to use the measured process variables typically available at farm-fed digesters, accounting for the effects of retention time, temperature and imperfect mixing. The estimation of the operational revenue and costs allow the owners to assess the most profitable approach to run the process. This would support the sustained use of the technology. The calculator is first compared with literature reported data, and then applied to the digester unit on a UK Farm to demonstrate its use in a practical setting.
Camacho J.,University of Granada |
Padilla P.,University of Granada |
Diaz-Verdejo J.,University of Granada |
Smith K.,Perceptive Engineering Ltd. |
Lovett D.,Perceptive Engineering Ltd.
Chemometrics and Intelligent Laboratory Systems | Year: 2011
In this paper, a new method to approximate a data set by another data set with constrained covariance matrix is proposed. The method is termed Approximation of a DIstribution for a given COVariance (ADICOV). The approximation is solved in any projection subspace, including that of Principal Component Analysis (PCA) and Partial Least Squares (PLS). Given the direct relationship between covariance matrices and projection models, ADICOV is useful to test whether a data set satisfies the covariance structure in a projection model. This idea is broadly applicable in chemometrics. Also, ADICOV can be used to simulate data with a specific covariance structure and data distribution. Some applications are illustrated in an industrial case of study. © 2010 Elsevier B.V.
Goldrick S.,Northumbria University |
Goldrick S.,University of Manchester |
Goldrick S.,Perceptive Engineering Ltd |
Stefan A.,University of Manchester |
And 4 more authors.
Journal of Biotechnology | Year: 2015
This paper describes a simulation of an industrial-scale fed-batch fermentation that can be used as a benchmark in process systems analysis and control studies. The simulation was developed using a mechanistic model and validated using historical data collected from an industrial-scale penicillin fermentation process. Each batch was carried out in a 100,000. L bioreactor that used an industrial strain of Penicillium chrysogenum. The manipulated variables recorded during each batch were used as inputs to the simulator and the predicted outputs were then compared with the on-line and off-line measurements recorded in the real process. The simulator adapted a previously published structured model to describe the penicillin fermentation and extended it to include the main environmental effects of dissolved oxygen, viscosity, temperature, pH and dissolved carbon dioxide. In addition the effects of nitrogen and phenylacetic acid concentrations on the biomass and penicillin production rates were also included. The simulated model predictions of all the on-line and off-line process measurements, including the off-gas analysis, were in good agreement with the batch records. The simulator and industrial process data are available to download at www.industrialpenicillinsimulation.com and can be used to evaluate, study and improve on the current control strategy implemented on this facility. © 2014 Published by Elsevier B.V.
O'Brien M.,Perceptive Engineering Ltd. |
Mack J.,Perceptive Engineering Ltd. |
Lennox B.,University of Manchester |
Lovett D.,Perceptive Engineering Ltd. |
Wall A.,United Utilities
Control Engineering Practice | Year: 2011
This paper details a case study application of model predictive control for a wastewater treatment process in Lancaster, North England. The control system was implemented in real time, together with a plant monitoring system for the purposes of process supervision. Following implementation, the model predictive control system provided significant benefits compared with the previously applied control system. These benefits included a reduction of over 25% in power usage and a similar increase in plant efficiency. The system therefore represents a useful tool in helping the water industry to reach its goal of significantly reducing its carbon footprint. © 2010 Elsevier Ltd.