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DeNovo, an independent upstream operating company based in Trinidad and Tobago, was seeking to leverage the best technology commercially available to run optimized operations that support innovation, enhanced workflows, and industrial efficiency. The company manages complex operations with a wide range of risk profiles and needed to be agile and highly efficient to remain safe, competitive, and profitable. Operating inefficiencies could account for as much as 25% loss in production output and up to a 40% increase in energy and operational costs.

The customer, a diversified energy company with operations in refining, marketing, midstream, chemicals and specialties, had experienced three previous failures of a hydrogen compressor resulting in millions in production losses and additional maintenance costs. The company had begun its own digital transformation initiative that uses big data, machine learning and artificial intelligence (AI) to drive cultural change in the organization. As part of the initiative, they were investigating predictive maintenance. The customer decided to organize a competitive bakeoff, trimming an initial list of ten predictive analytics vendors to a handful of finalists. Ultimately, AspenTech was chosen as the sole vendor to execute an online pilot project.

Braskem, a major player in the international petrochemical market, was facing challenges with variability in product quality, high energy usage, and excess reflux in their operations. The company had around 210 AspenTech advanced process control (APC) controllers installed among four Brazilian cracker sites. However, up until 2015, advanced process control was only installed on furnaces. The lack of an optimization algorithm on distillation columns resulted in lost potential benefits and unnecessary excess spending. There were frequent composition peaks and an excess of reflux, meaning excess utility spent on refrigeration compressors that use high pressure steam. Braskem decided to implement an optimization strategy using Aspen DMC3 to improve product quality and reduce project costs.

One of the world’s largest fully integrated zinc and lead smelting and refining complexes wanted to improve their metallurgical operations. The team recognized they had an opportunity to improve preventative maintenance by using information from their process signal historian. In addition, they wanted a solution that could help as the company developed a comprehensive approach to strengthen environmental, employee and community safeguards. The operations group’s reliability team needed a technology to track, detect and prevent equipment failures.

GlaxoSmithKline (GSK), a global healthcare company, was facing challenges with its batch production record and associated workflows. The company was dealing with a massive volume of business, with multiple packaging lines handling upwards of 10,000 batches per year, each batch record including over 1,000 manual entries. This resulted in over 10 million manual record entries per year. The preparation and review time for each batch was 10 days. GSK wanted to review the structure of its batch production record and associated workflows as part of a continuous improvement process. The goal was to reduce batch review time, which would result in faster batch cycle time, higher throughput at the production facility, and faster cash-to-cash cycle time.

Organizations are hamstrung by traditional estimating methods. The challenge is to move to the model-based Aspen Capital Cost Estimator (ACCE) approach while smoothly and simultaneously improving consistency and accuracy of ongoing cost estimates, bid responses, and lump sum proposal development. Most business leaders, project directors, and bid managers know that they are hamstrung by traditional estimating methods. The challenge is to move to the model-based Aspen Capital Cost Estimator (ACCE) approach while smoothly and simultaneously improving on the consistency and accuracy of the ongoing estimates.

YPFB, the national oil company in Bolivia, was experiencing a significant decline in production in two of its major gas fields, San Antonio and San Alberto. The fields were operating at full capacity to meet market demands, while projects for well compression were still under development. The company needed to increase production to meet the growing demand for natural gas in South America, particularly in Brazil and Argentina. Additionally, YPFB had to maintain regulatory compliance while covering the internal market.

Chevron, a global energy company, faces challenges in maintaining and maximizing APC applications. After a unit turnaround, which involves maintenance, modification, overhaul, inspection, testing, and replacement of process materials and equipment, it is often difficult to return a controller to service without significant rework. The traditional process of managing plant step tests, collecting and cleaning data, and creating and evaluating the model is manual and time-consuming. With many controllers to support and a scarcity of engineering resources, Chevron faces a growing challenge in maintaining APC applications.

The mining company was seeking a step change in how to proactively handle reliability issues for critical equipment. Previous efforts with solutions like Smart Signal had not provided the benefits they were seeking. The company was interested in Aspen Mtell and visited an installation in Botswana to see it in action. They decided to conduct an online pilot which would provide a faster time to value.

Shell, a global group of energy and petrochemical companies, was facing inefficiencies and limited flexibility due to the independent operation of its many refineries. The actions of one refinery could adversely affect another in the same region, leading to higher costs and lower overall profits. Each refinery conducted business in its unique way to increase profit margin, but such 'siloed' operations often resulted in inefficiencies and lower margins. Shell identified uncommon operating procedures at each of its refineries, which led to these inefficiencies and lower margins.

PETROLTERV, a Hungarian engineering design and consulting company, was tasked with revamping the gas treatment technology at Hungary’s largest underground gas storage facility to meet new European (EN) standards. The new standards imposed more stringent hydrocarbon and water dew point requirements for natural gas handling systems than what was previously prevailing in Hungary. The site had three gas treatment trains each employing a TEG (Triethylene glycol) dehydration unit and a low temperature separation (LTS) unit which employed a propane refrigeration system and brazed aluminum heat exchangers (BAHX).

The petrochemical company was facing significant losses due to light component losses that go to the bottom of a fractionation column and pressurize the downstream column. This pressure resulted in downstream column production being lost to the flare. This stream contained a very valuable product, and the loss represented more than $1M USD. The company was looking for a solution to understand and resolve this production problem faster to limit the losses.

Glanbia Food Ingredients’ facility in Ireland processes about 1.4 billion liters of milk annually into butter, cheese, milk proteins, and whey derivatives. Anticipated increases in milk supply meant that the plant would not meet processing demand in the near future, so Glanbia sought ways to improve production throughput in order to meet new expectations. Management decisions were based primarily on anecdotal information. Whenever a problem arose, managers would dig through log sheets and spreadsheets and talk to operators and engineers in an effort to determine the root cause of the problem. Each area had its own way of capturing the data and calculating and presenting performance metrics, which led to inconsistent and unreliable information. This lack of credible information resulted in less-than-optimal operating decisions. Improvement opportunities went unnoticed or were never acted upon because no one could determine the root cause of the problem.

Linde’s U.S. engineering organization was facing high estimating variances and overruns, coupled with a large, dispersed estimating team. The problem was diagnosed as an inconsistent use of tools and business processes in the estimating function. The company was looking for a more strategic, centralized solution to streamline its estimating discipline, improve the accuracy of capital cost estimates, and achieve long-term cost savings.

Saudi Aramco, the state-owned oil company of the Kingdom of Saudi Arabia, was facing a challenge with one of its semi-conversion refineries. The refinery was producing excessive fuel oil, which was limiting the facility’s margin to a level lower than comparably sized refineries. The company decided to revamp the refinery to upgrade the bottom of the barrel products to create more value and improve the refinery’s profit margins. The refinery was also considering changing the crude oil it was processing. The revamp plan included adding new units and modifying existing ones.

INEOS, Europe’s leading independent crude oil refiner, processes more than 410,000 barrels of crude oil per day. Their production network spans 76 manufacturing facilities in 20 countries around the world. INEOS’ success is linked to a simple approach to business — a focus on customer satisfaction, total quality and reliability. When INEOS set its mission toward continuous improvement to become a low-cost producer of high-quality products, the heat exchange system became a priority because of its impact on productivity, costs and overall profitability. Fouling in each heat exchanger and the entire heat exchanger train is a common problem for refineries. Without proper monitoring and insight, refiners resort to reactive rinsing and cleansing operations, significantly disrupting the safe, efficient operation of plants — and costing them millions of dollars in lost revenue.

PETRONAS Melaka Refineries faced the challenge of creating an integrated refinery scheduling model to eliminate many standalone spreadsheets and leverage multi-user interaction under a single network. The process was long and tedious, and led to different versions of the same schedule — which increased the likelihood for errors and resulted in less than optimal crude production.

S&B Engineers and Constructors Ltd., a Houston-based EPC, has been facing challenges due to shrinking client budgets and tighter project schedules. Clients are demanding more for less, including more detailed digital handover of data to accompany traditional project deliverables. To remain competitive, S&B needed to find a solution that was faster and more tailored to the process industries than the traditional approach of producing project estimates.

A major U.S. transportation company was facing significant losses due to undetected catastrophic failures of locomotives. These line-of-road (LoR) engine failures were costing the company over a million dollars each in repairs, additional operational costs, and fines. The company's existing reliability techniques were not sufficient to detect these failures in time, leading to disruptions in the delivery of customer goods and impacting the company's reputation for safety and reliability.

Hyundai Oilbank was facing a challenge with their FCC (Fluid Catalytic Cracking) unit yield. The yield was not matching the planned yield as the plans did not consider changes in feed quality. This discrepancy was causing inefficiencies in their operations and was a potential source of financial loss. The company needed a solution that could accurately factor in feed changes and optimize operations accordingly.

The company, a leading Asian refining company, was facing challenges in reducing planning run times while increasing solution quality. They wanted to gain time to explore complex, multiple price and operation scenarios and eliminate the occurrence of local optima. The company also wanted to increase internal customer satisfaction through faster response times to different business processes and have the ability to capture and explore more opportunities. The decision to expand the use of Aspen PIMS-AO was due to the recently enhanced global optimization capabilities with the new Aspen PIMS-AO V8.7 algorithm and the intention to explore the parallel processing feature for the maximum business impact.

The Siam Cement Group (SCG), the second largest company in Thailand, sought to enhance its existing production capabilities by implementing innovative non-linear Advanced Process Control (APC) software. The focus of this project was on the downstream petrochemical production lines of Thai Polyethylene Co., Ltd. & Thai Polypropylene Co., Ltd. These facilities produce a wide range of polyethylene, polypropylene, and high-value added products. The challenge was to drive enterprise value and maximize return on assets with non-linear APC and enable engineers to gain proficiency and extend APC applications across multiple polyolefin manufacturing units.

Phillips66, a leading integrated oil producer, invests millions of dollars a year in refining capital projects, which can include maintenance, clean fuels upgrades, and growth investments. Completing these projects in a timely, efficient manner will help the company meet its operational goals and significantly improve the bottom line. However, with market dynamics keeping refinery loads at over 90% capacity and regulatory pressures requiring clean fuels and emissions upgrades, Phillips66 faces a steady growth of downstream capital investment projects. The company adheres to three principles for each project: capital discipline, operational excellence and execution, and financial optimization. That’s why the estimating function—now managed through Aspen Capital Cost Estimator—is so critical in examining the volume of capital proposals, and providing estimates earlier in the process.

Fluor, a leading engineering and construction company, faced a challenge in modeling sulfur recovery units (SRUs) with both liquid elemental sulfur and vapor allotropes. The process typically required several simulation files which were iterated manually to balance material and energy recycle loops required in the COPE II process. Multiple files were generally required because of an inability to use multiple property packages in a single simulation. This manual adjustment of the recycle streams was time-consuming and introduced room for error. Fluor needed a solution that could integrate multiple property packages into a single simulation and offer several other benefits for their SRU COPE II modeling.

Criterion’s goal was to implement a planning and scheduling solution that eliminated legacy tools and transformed their Sales & Operations Planning (S&OP) process into a world-class operation. However, significant challenges within their supply chain and scheduling processes had to be addressed. Prior to the use of Aspen Plant Scheduler, site scheduling was onerous and time-consuming, so the scheduling time horizon was limited to less than three months. Prior to a rigorous S&OP process, demand was also not represented very far into the future. These factors combined to provide an inaccurate view of available capacity against which supply chain had to perform multiple, daily ad-hoc quote feasibility (Can we do it?) analyses, leading to occasional over-committed situations.

Oxiteno, a leading manufacturer of surfactants and chemicals, needed a plant-wide model to evaluate options that could increase plant capacity and quickly and accurately respond to market demand. With constant fluctuations in feedstock and energy prices, reacting to economic changes quickly was vital to capitalize on the most profitable assets. In the past, unit operations were modeled separately and case studies could not be analyzed quickly and accurately. It was very difficult to increase plant throughput by evaluating individual pieces of equipment without the ability to examine the effect of changing process variables on downstream processes.

Cabot, a global specialty chemicals company, operates 39 manufacturing plants across 19 countries. The globalization of Cabot’s business posed a significant challenge for the manufacturing function. Independent site-based groups needed to transform into an integrated global organization. Access to information from different sources — including manufacturing, the supply chain and financial systems — was vital to make better, faster business decisions. An MES could help with the transformation by integrating and visualizing information across the enterprise. To help overcome the challenge and realize the business potential, Cabot launched an MES visioning process to define the direction and business advantages for their manufacturing systems.

Cabot’s Carbon Black business was using their own Microsoft® Excel and Access-based tools for scheduling, which did not provide the visibility required to make timely business decisions. Planning was performed regionally, scheduling was performed offline through customized tools at each plant, and executing events was done manually in the ERP system. As a result, schedulers were forced to spend the majority of their time gathering the data needed to make decisions. Cabot sought to improve the information available to the schedulers and enrich communication along the supply chain.

The company initiated a production planning and scheduling project as part of a major initiative focused on process re-engineering and automation. The overall goal of the production planning and scheduling project was to support several business initiatives. These included replacing the manual scheduling process, improving customer service, optimizing raw material and finished products inventories, reducing costs associated with off-specification quality, campaign transitions, packaging, and logistics and distribution, sharing information and improving cooperation between marketing/sales and production planning functions, and integrating planning and scheduling with the ERP system.

A large producer of synthetic rubber had been having quality issues with its batch products. These quality issues were resulting in significant revenue loss, as the company often needed to either reprocess the material or sell it for a lower price than expected. The producer was unable to determine what was causing the batches to be out of spec. The company was investigating issues with a reactor process that brings together ingredients to manufacture synthetic rubber. There were multiple reactors that performed this process, but the Aspen ProMV project would focus on the production of one reactor.