£330m purchase of sole aluminium smelter in UK opens door for industry
A bright new industrial future for the Scottish Highlands was heralded today (Monday 19th Dec) as Liberty House and SIMEC - both members of the GFG Alliance - completed a £330million deal with Rio Tinto to buy Britain's last remaining aluminium smelter at Fort William in Lochaber, together with the iconic hydro-power plants at Fort William and Kinlochleven, and associated estate lands.
During a visit by First Minister Nicola Sturgeon and Rural Economy Secretary, Fergus Ewing to celebrate the successful Scottish Government-backed sale, the new owners announced plans for a further £120million investment to upgrade equipment and establish an aluminium wheel manufacturing facility at the site. This will generate up to 300 jobs directly and hundreds more in the supply-chain.
Liberty aims to protect the existing 170 jobs in Lochaber and progressively expand metal manufacturing and downstream engineering there; eventually bringing up to 2,000 direct and supply-chain jobs to the heart of the Highlands and adding around £1 billion to the local economy over the next decade.
The acquisition includes the hydro-electric station and aluminium smelter at Fort William, the neighbouring hydro-plant at Kinlochleven and over 100,000 acres of estate land which hosts the water catchment area, including the foothills of Ben Nevis, Britain’s highest mountain.
Liberty – under the banner "Liberty British Aluminium" - will add substantial extra value to the production of aluminium by integrating the smelter with a new engineering and downstream manufacturing facility.
SIMEC will operate the hydro plants within its growing UK portfolio of renewable power assets. A key customer for SIMEC Lochaber Hydro will be the smelter, which is an intensive user of electricity to process alumina into aluminium.
This is one of the largest single investments yet made by the global GFG Alliance businesses. The acquisition marks a major step towards the delivery of GFG’s plan to forge a competitive and sustainable metals and engineering sector in the UK by integrating the supply chain and particularly by powering these industries with SIMEC’s renewable energy production.
The GFG Alliance sees the Lochaber aluminium operation as fitting strongly into Liberty’s growing automotive industry focus. Liberty is already an important components vendor to the top UK vehicles manufacturers and is rapidly growing its Tier 1 capabilities.
The Scottish Government is supporting the GFG Alliance’s acquisition and its investment programme by guaranteeing the power purchases of the aluminium smelter for the next 25 years.
First Minister Nicola Sturgeon said: “This is a historic day for the UK’s last remaining aluminium smelter here in Lochaber. GFG Alliance’s buyout of the complex will protect 170 existing jobs and with ambitious plans to invest in the site, expand operations and add value, we look forward to hundreds of new jobs being created in the coming years.
“The Scottish Government is supporting GFG by guaranteeing the power purchases of the aluminium smelter, which reinforces the essential link between the smelter and hydro station at Fort William and provides a firm foundation for GFG’s ambitious expansion plans. Today is the start of an exciting new chapter in Scotland’s manufacturing story and the Scottish Government and its agencies will keep working with Sanjeev Gupta and the GFG Alliance to help them realise their enterprising vision for Lochaber.”
Sanjeev Gupta, executive chairman of Liberty House Group and of the GFG Alliance strategic board, said: “We hope this day will come to be recognised as the start of a bright new future for Highland industry. It puts Lochaber right at the heart of our vision for sustainable and integrated local production that can revitalise British manufacturing. The Scottish Government has recognised the immense opportunity this investment brings. Their support has been refreshing and inspiring.
“We look forward to working with the highly-skilled management team and workforce who join our family today and the many others who will join us in the future, as we embark upon this exciting journey here in the Highlands,” he added.
Jay Hambro, Chief Investment Officer of the GFG Alliance, and Chief Executive of SIMEC Energy & Mining Divisions said: “I am delighted that SIMEC’s portfolio of renewable energy assets continues to expand with a determined and focused investment strategy. These hydro-power stations have enough capacity to power around 83,000 homes. Today Lochaber provides the power required to produce 47,000 tonnes of aluminium. We have already identified investment programmes to significantly increase power generation from the existing assets and are studying how to create further capacity locally. SIMEC prides itself on providing innovative renewable and cost-effective power solutions for Liberty’s industrial activities; for example, the fish-oil powered generators driving the rolling mill at Liberty Steel Newport.”
He described the vast estate lands around Lochaber as a ‘sleeping giant’ and said: “SIMEC will look to work with Scottish Government, Highlands and Islands Enterprise and all local communities to develop the great potential locked up here.”
The purchase of Lochaber represents a major escalation of the GFG Alliance’s investment in Scotland, following Liberty’s acquisition of the Dalzell and Clydebridge Steel plants earlier this year. Dalzell formally restarted in September after being mothballed by previous owners.
Get in touch with our editor Dale Benton at [email protected]
McKinsey: adopting a smart approach to big data
Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.
According to a new McKinsey report, using months or even years’ worth of information, analytics models can tease out efficient operating regimes based on controllable variables, such as pump speed, or disturbance variables, such as weather. These insights can be embedded into existing control systems, bundled into a separate advisory tool, or used for performance management.
McKinsey recommends that to succeed with AI, companies should leverage historical data via automation. They will need to adapt their big data into a form amenable to AI. This ‘smart data’ can improves predictive accuracy and support root-cause analysis. Additionally, bolstering and upskilling expert staff to manage the process can result in an EBITDA increase of 5 to 15%.
A common failure mode for companies looking to leverage AI is poor integration of operational expertise into the data-science process. McKinsey advocates applying machine learning only after process data have been analysed, enriched, and transformed with expert-driven data engineering using the following steps:
Define the process
Outline the steps of the process with experts and plant engineers, sketching out physical changes (such as grinding and heating) and chemical changes (such as oxidation and polymerization). Identify critical sensors and instruments, along with their maintenance dates, limits, units of measure, and whether they can be controlled.
Enrich the data
Raw process data nearly always contain deficiencies. Thus, creating a high-quality dataset should be the focus, rather than striving for the maximum number of observables for training. Teams should be aggressive in removing nonsteady-state information, such as the ramping up and down of equipment, along with data from unrelated plant configurations or operating regimes. Generic methods to treat missing or anomalous data should be avoided, such as imputing using averages, “clipping” to a maximum, or fitting to an assumed normal distribution. Instead, teams should start with the critical sensors identified by process experts and carefully address data gaps using virtual sensors and physically correct imputations.
Reduce the dimensionality
AI algorithms build a model by matching outputs, known as observables, to a set of inputs, known as features, which consist of raw sensor data or derivations thereof. Generally, the number of observables must greatly exceed the number of features to yield a generalized model. A common data-science approach is to engineer input combinations to produce new features. When combined with the sheer number of sensors available in modern plants, this necessitates a massive number of observations. Instead, teams should pare the features list to include only those inputs that describe the physical process, then apply deterministic equations to create features that intelligently combine sensor information (such as combining mass and flow to yield density). Often, this is an excellent way to reduce the dimensionality of and introduce relationships in the data, which minimize the number of observables required to adequately train a model.
Apply machine learning
Industrial processes can be characterized by deterministic and stochastic components. In practice, first principle–based features should provide the deterministic portion, with machine-learning models capturing the statistical portion from ancillary sensors and data. Teams should evaluate features by inspecting their importance and therefore their explanatory power. Ideally, expert-engineered features that capture, for example, the physics of the process should rank among the most important. Overall, the focus should be on creating models that drive plant improvement, as opposed to tuning a model to achieve the highest predictive accuracy. Teams should bear in mind that process data naturally exhibit high correlations. In some cases, model performance can appear excellent, but it is more important to isolate the causal components and controllable variables than to solely rely on correlations. Finally, errors in the underlying sensor data should be evaluated with respect to the objective function. It is not uncommon for data scientists to strive for higher model accuracy only to find that it is limited by sensor accuracy.
Implement and validate the models
Impact can be achieved only if models (or their findings) are implemented. Taking action is critical. Teams should continuously review model results with experts by examining important features to ensure they match the physical process, reviewing partial dependence plots (PDPs) to understand causality, and confirming what can actually be controlled. Additional meetings should be set up with operations colleagues to gauge what can be implemented and to agree on baseline performance. It is not uncommon for teams to convey model results in real time to operators in a control room or to engage in on-off testing before investing in production-grade, automated solutions.
Industrial companies are looking to AI to boost their plant operations—to reduce downtime, proactively schedule maintenance, improve product quality, and so on. However, achieving operational impact from AI is not easy. To be successful, these companies will need to engineer their big data to include knowledge of the operations (such as mass-balance or thermodynamic relationships). They will also need to form cross-functional data-science teams that include employees who are capable of bridging the gap between machine-learning approaches and process knowledge. Once these elements are combined with an agile way of working that advocates iterative improvement and a bias to implement findings, a true transformation can be achieved.