PROJECT
洗净植物优化
推动河流运营的供电加工改进
为河流运营（来源）拥有多年来拥抱创新，自动化流程来实现业务效率。现在，通过Race21™，他们正在接受程序的“连接”支柱，并开发利用现有传感器和仪器的数字系统，并采用先进的分析，帮助推动吞吐量和产量的提高。
This next phase of technology advancements at FRO is targeting wash plant optimization—a key step in the steelmaking coal processing cycle—by creating an operator advisory tool that recommends ideal set points in the wash plant, based on an analysis of incoming material and historical data. By reacting sooner to changes in the material and adjusting set points accordingly, yield is improving which is expected to achieve sustainable EBITDA value over time.
In addition, insights provided by the advanced sensing and analytics have also unlocked additional value, by identifying improvements to the thickener process, allowing the FRO wash plant to process additional coal from Greenhills Operations, thereby optimizing its processing capacity.
“Working with the RACE21™ teams’ resources and expertise has been instrumental in getting us set up on Google Cloud so that we can harness the power of big data,” says Shane McColman, Senior Engineer, Process Supervisor at FRO. “Working with RACE21™, we’re much better able to accelerate how we assess, model and implement changes to our systems.”
“This is an exciting ‘next step’ for us and an area that can drive real results in our business.”
PROJECT
磨削分析
大数据在高地山谷铜中推动新的收益
轧机优化需要不断评估和改进许多子过程，以确保最大的生产率;矿石有效地加工，优化铜回收。在高地山谷铜（HVC）操作中，大数据和机器学习正在开辟该地区的尖端机会，随着RACE21™的支持，HVC最近采用了两个优化项目，具有最大的提供价值的潜力。
选择半自动（SAG）磨机和批量浮选优化作为目标的过程，基于数据的准备和质量来实现高级分析。通过使用来自波士顿咨询集团（BCG）的Race21™团队和数据科学家（BCG），HVC开发了一种新颖的强大的工具，使用机器学习，以在最佳研磨和浮选操作条件下为运营商提供实时建议。
Early estimates project these improvements will create significant value by increasing copper throughput and recovery.
“We’re able to better use the data coming from the mine to determine optimal operating set points,” says Murray Cruickshank, Deployment Specialist, Technology and Innovation. “On top of that, we’re using Cloud-based technology to share these insights with operators, via a dashboard, so they can continuously evaluate and make real-time decisions.”
And a key aspect of this project has been working closely with operators throughout the process—keeping them informed of upcoming changes and receiving continuous feedback along the way.
“从操作员输入的支持和people who use these tools everyday—has been critical to ensuring the testing goes smoothly,” adds Murray, “The operator buy-in to field-test the tool has been strong, which will help guide our next steps as we move to future phases and roll out the tool more broadly.”
Khushaal Popli, Specialist, Process Control, HVC, who has been working closely with data scientists from BCG, is encouraged by the early results and even more excited about how these investments can help pave the way for step changes in predictive maintenance—predicting and mitigating equipment failures, and thereby minimizing unexpected downtimes.
“We are at the leading edge of revolutionizing our processes so that we can work smarter, faster, and most importantly, safer,” says Khushaal.
PROJECT
Condition Based Monitoring
数据科学的进展提升了TRAIL操作的预测性维护
Predictive maintenance presents enormous opportunities for operations to achieve innovation-driven efficiencies, by using sensors and powerful tools to analyze data in real time so that equipment is used optimally. At Teck’s Trail Operations, where they’ve been using machine learning for several years, RACE21™ is providing the resources and expertise to accelerate work in their predictive maintenance program, allowing the team to react more quickly and reduce maintenance costs.
“几年前，我们开始基于基础的基础监测;使用来自传感器的数据与我们的设备建立趋势。现在，通过支持RACE21™和麦肯锡，我们能够在数据科学，即先进的分析中申请进步，使我们的预测维护软件更加强大，“高级可靠性专家，TRAIL操作。“现在，我们可以检测到失败，有计划的工作订单和准备好的修复，一切都是一般的 - 这种效率让我们在整个新装备中工作。”
“RACE21™ is empowering us to think differently about how we can enhance performance in all areas—from safety, environment and production. The opportunities are pretty exciting,” adds Gordon.
