REFINING GAS PROCESSING PETROCHEMICALS ptq Q3 2022
CHASING HIGH MARGINS PETROCHEMICAL INTEGRATION SEAMLESS REFINERY &
RESOLVING NAPHTHA DEFICITS
FIRED HEATER OPTIMISATION
MAKE EVERY MOLECULE MATTER
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Q3 (Jul, Aug, Sep) 2022 www.digitalrefining.com ptq PETROLEUM TECHNOLOGY QUARTERLY
5 The energy transition: maybe later? Rene Gonzalez
7 ptq&a
emissions reduction Philippe Mège and Michel Molinier Axens
23 Capital priorities in the downstream industry Rene Gonzalez Editor, PTQ 29 Enhancing flexibility in two-stage hydrocrackers Devansh Dhar, Pronit Lahiri and Paul Ronald Robinson Topsoe
39 A step change in feed nozzle design Todd Foshee Shell Catalysts & Technologies
47 Biofeed co-processing for fuel production Scott Sayles, Matthew Caserta, Stephen DeLude and Al Keller Becht 55 Stationing an operator at a pressure safety valve bypass Jonathan R Webber Fluor Canada Ltd 63 Role of FCC process and catalysts in the energy transition Maria Nieves Alvarez MERYT Catalysts & Innovation 69 Challenges with carbon disulphide removal in petrochemical naphtha Manish Bhargava, Anju Patil and Niyaz Ahmad DWC Innovations 75 Optimising oil and gas operations – when to decommission or modernise Stuart Querns Delaware United Kingdom 79 New graphical procedure to design reactor supports used in industry Osama Bedair Consultant
85 FCC unit yield improvement with artificial intelligence Geraldine Hwang and Abishek Mukund Imubit
91 Production optimisation for refining and olefins: how best to achieve it Ron Beck AspenTech
94 Technology in Action
Cover The industry’s pursuit of high-value outlets requires optimisation of process infrastructure at levels not seen in previous margins cycles
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Expand your renewable feed options with Albemarle ReNewSTAX ™ . ReNewSTAX ™ – Albemarle’s catalyst technology for processing renewable feeds into your valuable hydrotreating unit. Learn how you can power the potential of your refinery at Albemarle.com/Renewables .
Vol 27 No 4 Q3 (Jul, Aug, Sep) 2022 ptq PETROLEUM TECHNOLOGY QUARTERLY
The energy transition: maybe later?
C ertain refiners report high margins in an otherwise flat-margins fuel market. The current 3 million bpd crude oil production deficit caused by policies and regulations affecting the market has led to the highest recorded fuel prices. The ideal scenario is to capture high fuel margins while they last and stay on course towards expansion of the petrochemicals value chain, preferably with a staged investment approach. Brent crude oil prices will average $140/bbl this autumn, according to Goldman Sachs. Nevertheless, fuels and petrochemicals from fossil feedstocks are expected to dominate energy markets for decades. In parallel, the sages have spoken loudly that electric vehicles (EVs), renewable fuels, and biofuels would provide a better and somewhat less expensive energy alternative with zero-emissions benefits. Green energy promoters may have overlooked the cyclic effect on GHG emis- sions from other industrial sectors like mining and its importance in the extraction of precious metals needed for EVs. For example, the projected transition to EVs isn’t happening largely due to a shortage of precious metals (cadmium, cobalt, lith- ium, platinum). Mining costs are increasing in proportion to diesel costs used to run heavy machinery and remote power grids favoured in metals extraction operations. Like the global microchip shortage, precious metal supply is insufficient to sustain an expanded EV market. Estimates are that each EV requires about 2.2 lb (1.0 kg) of precious metals. EV transaction prices are exceeding $60,000, which is one reason why demand for fossil fuels and their well-established supply and distribution infrastructure is preferred. Nevertheless, many hydrocarbon and chemical processing innovators concur that global CO 2 emissions are too high (exceeding 34 billion tpy) and are investing in natural gas, renewables, and biofeedstock for near-zero emissions. These developments are why PTQ is a strong supporter of the October Refining India 2022 Conference, providing an opportunity to meet experts on increasing fuels production, improving energy efficiency, and directly linking refinery opera - tions with the expanding petrochemical value chain. For example, there is a busi- ness interest in reconfiguring a refinery’s final conversion sections. In expanding markets such as China and India, there is scope for reconfiguring refinery assets towards naphtha production to maximise feedstock for downstream aromatics production while making efforts to secure high margins in the fuels mar- ket. But with naphtha production constraints, other fuel production alternatives, such as gasoline and diesel, need to be considered. In fact, opportunities emerge to leverage existing hydroprocessing and FCC operations for renewable fuels and biofuels production while maintaining high run rates. Biofuels’ high oxygen levels and efficient combustion for lower emissions are important factors for sustainability-focused investors to weigh when reviewing new technology. Petrochemical margins in the post-pandemic recovery are approaching $1400 per tonne compared to $550 per tonne for transportation fuels. Let’s not overlook that adoption of automation on a large scale is increasing human-machine interac- tion at those refinery facilities with high sustainability rankings, leading to better returns. More importantly, the execution of these connectivity strategies is foremost to linking renewable and biofuels processes to plant utilities and new petrochemical assets, to be discussed in the PTQ Revamps special report.
Editor Rene Gonzalez editor@petroleumtechnology.com tel: +1 713 449 5817 Managing Editor Rachel Storry rachel.storry@emap.com Graphics Peter Harper Digital Editorial Assistant Ciaran Nerval US Operations Mark Peters mark.peters@emap.com tel: +1 832 656 5341 Business Development Director Paul Mason sales@petroleumtechnology.com tel: +44 7841 699431 Managing Director Richard Watts richard.watts@emap.com Circulation Fran Havard circulation@petroleumtechnology. com
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Vacuum tower cutpoint delivers profits
Cutpoint Concerns
poorly designed heaters may experience coking with COT below 700°F (370°C).
Crude unit vacuum tower performance is often critical to a refiner’s bottom line. e vacuum tower bottoms stream is valued far below the gas oil cuts, so most refineries look to minimize it. Many vacuum columns are also designed or revamped to produce a diesel cut, recovering diesel slipped from the atmospheric column that would otherwise be downgraded to VGO product. Good vacuum column performance can maximize the profitability of downstream units by removing distillate hydrotreater feed (diesel) from FCCU or hydrocracker feed (VGO) and removing VGO from coker feed (resid). One important measure of vacuum column performance is VGO/resid cutpoint. e cutpoint is the temperature on the crude TBP curve that corresponds to the vacuum tower resid yield. Vacuum column cutpoint depends on three variables: 1. Flash zone temperature 2. Flash zone pressure 3. Stripping section performance (if present) Flash zone temperature is driven by vacuum heater coil outlet temperature (COT). Increasing COT increases cutpoint. Vacuum heater outlet temperature is typically maximized against firing or coking limits. When processing relatively stable crudes, vacuum heaters with better designs and optimized coil steam can avoid coking even at very high COT (800°F+, 425°C), but
Flash zone pressure is set by vacuum system performance and column pressure drop. Lower flash zone pressure increases cutpoint until the tower shell C-factor limit is reached, at which point the packed beds begin to flood. Vacuum producing systems are mysterious to many in the industry, so a large number of refiners unnecessarily accept poor vacuum system performance. With technical understanding and a good field survey, the root causes of high tower operating pressure can be identified and remedied. In columns with stripping trays, stripping steam rate and tray performance are important. Stripping steam rate is limited by vacuum column diameter (C-factor) and vacuum system capacity. Any steam injected into the bottom of the tower will act as load to the vacuum system, so vacuum system size, tower operating pressure, and stripping steam rate must be optimized together. Depending on the design, a stripping section with 6 stripping trays can provide between zero and two theoretical stages of fractionation, which can drive a big improvement in VGO yield. Although the variables for maximizing vacuum tower cutpoint are simple, manipulating them to maximize cutpoint without sacrificing unit reliability is not. Contact Process Consulting Services, Inc. to learn how to maximize the performance of your vacuum unit.
3400 Bissonnet St. Suite 130 Houston, TX 77005, USA
+1 (713) 665-7046 info@revamps.com www.revamps.com
pt q&a
More answers to these questions can be found at www.digitalrefining.com/qanda
Q Considering more stringent NOx reduction require- ments in the refining and petrochemical industry, what are the optimal strategies for reducing NOx emissions from process heaters and SMR/PSA heaters? A Ron Beck, Senior Director, Industrial Marketing, AspenTech, ron.beck@aspentech.com and Gerardo Munoz, Product Manager, Engineering, AspenTech, gerardo.munoz@aspentech.com There are several key aspects of reducing emissions: • Accurate measurement and monitoring to understand levels and trends and relate those to process conditions in a unit • Most effective removal of that pollutant from the flue gas or other emissions stream • Efficient process operation, which can minimise emis - sions from a unit or a process overall. Also, a pollutant such as NOx needs to be considered together with other plant parameters (carbon, SOx, energy, water use) because it becomes a multi-faceted optimisation problem to meet sustainability goals but still be profitable. Digital solutions are increasingly critical in making the right abatement decisions for emissions such as NOx, mainly because changing process operating parameters is faster and more cost-effective than adding more control systems. Improving efficiency by changing combustion conditions is essential to reduce emissions. Digital twin solutions and advanced process control (APC) apply open- and closed- loop modelling and optimisation technologies to stabilise operations, improve efficiency, and reduce and report emissions. AI technology can enhance rigorous models based on engineering first principles, using an approach called hybrid models to obtain highly accurate models to support engineering decisions. For example, Nissan Chemicals used AI to predict temperature profiles in an SMR for improving heat transfer, increasing efficiency, and reducing steam consumption. This would also reduce NOx. Advanced multivariate analytics can help understand which process conditions in a complex process may be the biggest culprits in conditions amenable to NOx creation (and reduction). Applying this methodology (for example, using AspenTech’s PRO MV multivariate technology) can identify strategies to minimise NOx (and other pollutants). Then, of course, a catalytic control unit, such as a selec - tive catalytic reduction (SCR) unit, is needed to remove the remainder to reach permitted limits. Again, a hybrid model, as previously described, can be instrumental in operating the pollution control systems to maximise effectiveness and catalyst lifetime. A Hervé Lavieu, Global Furnaces Product Line Vice President, Heurtey Petrochem Solutions, herve.lavieu@ heurtey.net There are several aspects to be considered:
• At the pre-combustion level, the solution of ‘fuel switch’, mostly meaning fuel oil (FO) to fuel gas (FG), appears to be beneficial. The switch from FO firing to FG firing will reduce the flame temperature and, as a result, reduce the NOx thermally generated. This switch will also be benefi - cial since, unlike the FO, the FG combustion will not contain organo-nitrogen compounds and chemically formed NOx will therefore be eradicated. • At the combustion level, accurate control of excess air will also reduce NOx formation. At the post-combustion level, the installation of a DeNOx system (SCR) remains the more efficient solution for NOx reduction. SCR is a post-combus - tion method dedicated to converting NOx into N₂ and H₂O by means of reaction with ammonia (NH3) in the presence of catalyst. SCR systems can achieve a reduction of up to 95+% of NOx in the flue gas stream. A Dirk Jan Treur, Mechanical Engineer, Becht, djant - reur@becht.com In case the current NOx emissions are above the proposed limits, a few different NOx reduction measures are avail - able. The first and preferred option is always to improve operational performance, which usually results in lower emissions, but in many cases these operational measures are either impossible or insufficient. A few operational measures are: • In case the burner design has primary and secondary air (air staging), decrease primary air. This will typically result in reduced flame temperature, which reduces NOx formation • Decrease air preheat temperature; however, this may decrease thermal efficiency • Reduce hydrogen content in the fuel mix, which is some - times an option for refinery fuel gas. Less hydrogen results in lower flame temperature, which reduces NOx formation • Water or steam injection may be used to cool down the flame, which reduces NOx formation. However, this pri - mary measure also reduces energy efficiency but can make sense when NOx emissions are just above the emission limit value. The second main option would be to install or replace the existing burners with new (ultra) low NOx burners, but the viability of this option depends on the firing duty, heat flux pattern, size of the heater, and firebox temperature. Low/ ultra-low NOx burners typically have larger flame volume. Firebox dimensions should be large enough to allow for lower NOx burners, which can be challenging in case of process heater revamping. Also, the firebox exit tempera - ture can increase with burner revamp projects, which may limit this option. The third and often most expensive option would be to install DeNOx flue gas cleaning equipment. This will typi - cally require additional space in the flue gas area and can reduce efficiency. DeNOx flue gas cleaning is a second - ary NOx control measure and is a post-combustion NOx
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control technology, also known as an ‘end of pipe’ solu- tion. Two widely used are the selective catalytic reduction (SCR) process and the selective noncatalytic reduction (SNCR) process. SNCR and SCR technology should be considered only when the available low-NOx combustion technology pro- vides insufficient NOx reduction to comply with the emis - sion requirements. Furthermore, DeNOx technology cannot be installed easily in any system and relies on sufficient space, proper mixing with injectant, flue gas temperature, and residence time. Both SNCR and SCR processes reduce NOx to N₂ and H₂O using ammonia or ammonia-based reagents like urea. The residual unreacted ammonia leaves the reactor as ammonia slip. The emission of ammonia slip is another pol- lutant, which typically also has legislative limits. Q With the enormous potential for reducing the refin - ery carbon footprint by increasing direct fired heater efficiency, what successful cases provide an example of increased efficiency and reduced emissions? A Ron Beck and Gerardo Munoz, AspenTech For most refineries and bulk chemical sites, the opportunity is there to improve energy efficiency by 10-30%, with a significant amount of that through optimisation of opera - tions. A recent benchmark of European refineries shows that they lag significantly behind best-in-class global refin - ers in energy efficiency. Some examples of how digital solu - tions are achieving energy efficiencies and reduced carbon emissions include: • KNPC identifies opportunities across the refinery to save 57,890 tpy of CO₂ and reach an EII rating of 3.2 without sig - nificant investment through rigorous modelling of energy supply and demand in Aspen Performance engineering. Additionally, it identified opportunities with added CAPEX that allowed it to save 260 MBTU/hr with an EII of 6 • Sinopec decreased by 13% the total fuel gas to unit feed ratio using APC (Aspen DMC3) at an integrated refining petrochemical complex • An international bulk chemicals producer reduced its fuel gas consumption by 1.7 to 4.3 kg/hr per day per furnace in less than a month using multivariate statistical analysis with Aspen ProMV • Tupras reduced fuel consumption by 20% through heat integration and higher energy recovery • Probably the broadest existing case study is the 10% car - bon reduction Dow Chemical has reported to the investor community over the past 10 years through the combined use of APC and digital twin models online, both using AspenTech technology. A Hervé Lavieu, Heurtey Petrochem Solutions To increase the fuel efficiency of existing heaters, several options can be considered depending on the inlet tempera- ture of the process fluid and the temperature of the flue gas rejected to the atmosphere. The first solution is revamping the heater to increase the heat exchange surface of the coil; this solution has been
implemented on natural draft heaters and does not require the installation of additional equipment such as air and flue gas fans. The second option consists of the installation of an air preheating system (APH) to preheat the combustion air by recovery of heat on the flue gas going to the stack. And when the heater is already equipped with such an APH, the installation of a new generation APH allows the recovery of more heat from the flue gas. In this situation, we have installed an APH with an enhanced exchange surface but also utilising special coated or polymer-based tubes, oper- ating below the acid dew point (ADP) to reach low stack temperatures (<90°C/195°F). Q What do you see necessary for successful implemen- tation of plant-wide AI and machine learning (ML) in the refinery and petrochemical complex? Can AI/ML strate - gies provide additional benefits beyond APC? A Ron Beck and Gerardo Munoz, AspenTech AI and machine learning are positioned to create very sig - nificant benefits in operational excellence. In fact, some of the benefit areas will be significantly greater than those that may come from APC. One area we have already implemented is in applying AI/ ML to refinery and olefins planning. AI can augment the existing planning systems by helping planners sift through hundreds of complex plan alternatives to find those most likely to achieve lower carbon operations based on results from historical planning periods. Another area is achieving more accurate digital twin models by applying AI to data streams to improve results from rigorous models. These will help in crucial areas of energy efficiency, such as heat exchanger and process unit fouling. By being able to model the actual conditions, not the ideal ones, these AI-based hybrid models will have a big impact on optimising operations for energy efficiency. It is necessary to have technology that democratises the application of Industrial AI, making it possible for AI and ML to be used by engineers of different skill levels. Very often, process data is treated as company intellec - tual property. As a result, access can be on a need-to-know basis. There must be documented processes for people who need that access to be able to gain it. Likewise, it is necessary to have good data quality. There may be time periods requiring users to ‘condition’ data to be able to get the most out of it, for example, where: • The process is not steady (assuming you are calibrating a steady-state model) • The process measurement is bad (for example, outside the measurable range of the instrument) • The data does not close the material balance • The plant is operating in a regime that is out of the norm. There are many benefits to using AI/ML. Aspen hybrid models combining first principles and AI/ML allow engi - neers to: • Capture unknown or unmeasurable details of phenomena while recalibrating models to changing process conditions more easily with AI/ML
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A FULL SPECTRUM OF BOTTOM OF THE BARREL TECHNOLOGIES
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• Represent real plant behaviour with models created from operational data and first-principles constraints • Create high fidelity models that can be used for rapid and accurate decisions in engineering and operations or to expand modelling scope • Better planning decisions to recapture benefits • Easily incorporate complex process units into the scope of closed-loop optimisation. A Damien Maintenant, Advanced Process Control Lead, Axens, damien.maintenant@axens.net By unifying planning/scheduling and APC and coordinating the controllers’ objectives, plantwide optimisation provides additional benefits beyond APC. AI/ML techniques can be used to simplify plantwide optimisation tools that are complex to operate and strenuous to maintain, but these algorithms need a large amount of data and have to be developed, guided, and monitored by engineers with rigor- ous knowledge of the process and the operation. In addition, in an increasingly connected global market context, refining and petrochemical schemes are more and more complex and integrated, complicating plantwide opti - misation. As a result, this is not only a matter of data science but also of process expertise, as it is of real importance to understand the interactions between the different process - ing units across the plant. Regarding tools and techniques, hybrid modelling, meaning a combination of historical oper- ating data and first-principles models, must be taken into consideration for developing such solutions. It is worth mentioning that some mandatory project phases must be respected. The first milestone is to express the objectives of the optimisation solution clearly; the second milestone is to accordingly define the strategy to achieve these objectives and make available all needed resources. Consider that agility is also key to redefining objectives or resizing resources as necessary. Agility is also a way to extend the scope of the solution over time by starting plantwide optimisation, for instance, of the utilities and hydrogen network, then including pools management, and so on. In conclusion, data availability and monitoring, profes- sional expertise (process, operation, control, data science), efficient project management, and resources (on the one hand, people and, on the other hand, the tools and tech - niques) are necessary for the successful implementation of plantwide optimisation using AI/ML. A Alvin Chen, Global Technology Application Manager, BASF Corporation, alvin.chen@basf.com, Mark Schmalfeld, Global Marketing Manager, BASF Corporation, mark.schmalfeld@basf.com Successful implementation of machines with artificial intel- ligence (ML/AI) in refinery or petrochemical complexes requires a deliberate and thoughtful targeted approach with clearly defined benefits, a robust and safe technology framework, and a clear economic benefit. These hydrocarbon processing facilities have some of the highest safety standards in the world, and the use of their technologies in ML/AI offers the potential for benefits, yet
they must achieve standards of safety and robustness that are often higher than many other industries. This can often set the speed of adoption in the industry, yet it cannot be missed as a tool because there are large potential benefits to operations that can be achieved with AI/ML. ML/AI offers a chance to continuously learn and improve, leading to better productivity, less downtime, and ulti- mately improved cost efficiency. Specific AI/ML strategies can provide additional benefits beyond automated process control (APC) by extending analysis to monitoring market conditions and adjusting crude acquisition plans. Additionally, it can allow catalyst formulation adjust- ments to accommodate market changes and uncertainty. ML/AI offers a chance to continuously learn and improve, leading to better productivity, less downtime, and ultimately improved cost efficiency A specific example is changing catalyst to move away from C₄= selectivity in favour of C₃/C₄ flexibility when there is uncertainty about which production will have the highest value and demand. An AI/ ML program can help in these types of decision- making recommendations while allowing a final human interface in the process. This is just one simple example of how these tools can be implemented, which is why today’s much more complex logistics, process control, and mainte - nance monitoring are being improved through AI/ ML. Q In the transition to digital worker and enablement solutions using predictive analytics and remote monitor- ing, how much input does the end-user have in creating the ‘dashboard’? A Ron Beck and Gerardo Munoz, AspenTech In this area of situational awareness, decision-support sys- tems (and we prefer to call them decision support rather than dashboards) are designed to support and assist end-users in making operating and strategic decisions. The best of these (for example, the situational awareness system from AspenTech’s OSI business) are designed for end-users to configure the visuals to their needs. They will need to be able to create displays ‘on-the-fly’ to help them quickly use the appropriate data and analytics to diagnose and solve operational problems faster and better. A Alvin Chen, BASF Corporation Without defining the specific ‘dashboard’ in question, in general, as systems get to be more complex and auto- mated, it becomes more important for staffers to monitor what the automation system is doing. This is especially important during the transition from a high level of human interaction to a lower level since the automation system will still be learning what it needs to do.
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ENERGY TRANSITION TO REFINING TRANSFORMATION
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From this standpoint, end-users must play an integral role in developing the dashboard, so they can monitor operations in a way that is efficient and requires minimum interaction with the automated system. Q Is there scope for significantly reducing fractionation furnace energy consumption for FCC and hydrocracker product recovery sections? A Gary Martin, Process Development Expert, Sulzer Chemtech, gary.martin@sulzer.com Refiners that fractionate FCC naphtha into three or more streams can take advantage of DWC technology to reduce the energy consumed by the process. This technology has been successfully applied for this application. Typically, this is where refiners are using a processing configuration to shift from fuels production to petrochemicals or using pro- cessing schemes to retain octane during sulphur removal. In doing so, not only does the DWC technology provide for lower energy consumption, but the shift in product slate when producing additional petrochemicals can have ben- efits in product upgrade value and credits. Q Considering that new catalyst formulations play a sig - nificant role in successful reactor performance, what else is necessary for optimal reactor/catalyst performance? A Ron Beck and Gerardo Munoz, AspenTech Operating conditions affecting catalyst performance include temperature, pressure, composition, and other aspects that can impact catalyst degradation. Accurate modelling solutions are crucial to monitor catalyst performance and troubleshoot operations to prevent deactivation. Because reactors have a complex series of chemical and physical reactions and dynamics going on inside, accu- rate modelling requires high fidelity and ongoing valida - tion and tuning. Traditionally, process licensors guard the proprietary nature of their technology and don’t release such models. Owners who want to be independent of the licensors’ reactors employ rigorous models to predict conditions leading to catalyst degradation or deactivation. Molecular- level modelling of crude characteristics, and conversely a more detailed understanding of reactor performance and catalyst interaction, is a key digital technology that helps in this area. But AI analytics can be a game-changer. AI is already changing the game in terms of the formerly time-consuming task of calibrating these models. Refiners using hybrid models in this area have seen it as a break- through in the practical use of these models to optimise Accurate modelling solutions are crucial to monitor catalyst performance and troubleshoot operations to prevent deactivation
catalyst economics. As biofeedstocks are blended into the refining process, this will only get more complicated. To fully optimise refining performance with respect to biofuels, carbon intensity, energy use, and catalysts, a com- bination of digital tools comes into play. Effective use of models that can predict unit fouling (rigorous engineering models in a digital twin mode) benefits from: • Planning models that can plan the blending of feedstocks and biofuels to achieve carbon, energy use, and margin results A synchronisation of the models is needed to achieve results that can be used to make the trade-off decisions needed. To provide that, AspenTech has innovated a concept we term model alliance. We use AI-based reduced-order models to synchronise across these different refining mod - els and enable the optimisation engineers, plant managers, and technical teams to achieve the desired optimisation. A Dinesh-Kumar Khosla, Global Market Manager Heavy Ends, HDC, Axens, dinesh-Kumar.KHOSLA@axens.net In units featuring fixed-bed reactors, along with optimum catalyst design, overall reactor/catalyst performance can be enhanced using high-efficiency reactor internals. Axens’ proprietary EquiFlow reactor internals ensure a uniform gas/liquid distribution and optimum mixing in the reactor, thereby minimising channelling and hot spots to ensure optimal use of the entire catalyst inventory in the reactor. This enhances catalyst activity, selectivity, and stability, minimising catalyst change-out frequency while ensur - ing safe and reliable operation. EquiFlow distributor trays employ a dispersive system located below a chimney tray to ensure close-to-ideal vapour/liquid distribution throughout the catalytic bed underneath. EquiFlow quench systems (i.e., the proprietary Hy-Quench-XM and Hy-Quench-NG) feature a more com- pact design. This results in smaller reactors in grassroots configurations and increased catalyst volume for exist - ing reactors. These quench systems provide higher ther - mal efficiency over a wider range of operating conditions. These systems result in longer catalyst cycles and/or higher throughput operation. For reactors prone to fouling, the EquiFlow smart filter - ing tray system (i.e., proprietary Hy-Clean) limits recurrent pressure drop problems while ensuring a perfect gas/liq - uid distribution in reactors. It will prevent plugging of the bed by catching and retaining feed impurities that are often responsible for crust formation between the different cata- lyst layers. Notably, with the use of Hy-Clean, there is no additional pressure drop compared to conventional dis- tributors or quench systems. Overall, Hy-Clean will enable a significant increase in catalyst cycle length, leading to higher profitability. The right combination of catalyst and reactor internals is thus essential for reliable and profitable reactor operation. A reduction in reactor operating temperature and pressure drop with Axens’ EquiFlow reactor internals also results in a lower CO₂ footprint associated with specific unit operation. • Energy and utility models • Mass balance accounting.
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Petrochemical complex digitalisation for greater efficiency and emissions reduction Concepts of automation and data exchange in manufacturing have propelled both upstream and downstream sectors into the 4.0 world, gradually extending to petrochemical plants
Philippe Mège and Michel Molinier Axens
N umerous benefits are expected from digitalisation transformation, including real-time business man- agement and associated efficiencies, minimised environmental impact, and improved sustainability and safety. In petrochemical complexes, process licensors and catalyst suppliers play a key role in accompanying this trans- formation as they provide the digital tools complementing their proprietary technologies, implement tailored users fea- tures such as diagnostics and alerts, predictive functions and improvement suggestions, as well as cybersecurity solutions to ensure the confidentiality of their real-time exchanges with operators. Digitalisation options must come at a competitive cost to ensure the benefits largely surpass the additional invest- ment. Process unit remote monitoring by licensors and catalyst suppliers used to be a challenge due to the time lag between data collection, data submission, data interpreta- tion and feedback transmission, and time zone differences. With real-time data display and exchange of operating sta- tus viewgraphs for a unit or an entire complex, Axens is in an ideal position to assist operators in maximising the efficiency of their processes, with benefits for both users and technol- ogy providers.
Against this backdrop, it benefits to show the advantages of digitalisation in an aromatics complex, where processes are inherently and strongly interconnected. Examples of performance visualisation by various units of measure, alert functions, optimisation tools, and performance prediction are presented together with yields improvements and ensu- ing benefits in commercially operating plants. The impact of digitalisation on GHG emission reduction is also detailed and discussed. Finally, the coming digital twin approach for a petrochemical plant is introduced. Digitalisation for the petrochemical industry Real-time business, real-time supply chain management and optimisation, production excellence, enhanced sustainability and compliance, and reduced risk to personnel safety and health were among the many expected benefits as industry 4.0 concepts caught up with the refining industry. 1 These improvements are now extending to petrochemical plants, even if many chemical companies are still figuring out how to apply digitalisation to their business in the most effective way. 2 Process licensors are in a unique position to accom- pany the digital transformation of petrochemical operations as they:
Light naphtha
Ra nate
Benzene
Light reformate
Benz/tol fractionation
Extraction
Transalkylation
CT
Full range naphtha
Naphtha hydroteating
NS
H
Paraxylene
Reformer
RS
Separation
Isomerisation
Heavy naphtha
Liquid phase isomerisation
XC
CT
Heavy reformate
HAR
Heavies
Figure 1 Example of aromatic complex block flow diagram
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•Master the processes they licence, hence are in the best position to design digital tools for these processes • Explain to users the possibilities and benefits associated with the digitalisation of their process units •Supply cost-effective, user-friendly digital applications, easy to implement in existing or new facilities •Provide valuable functionalities, including plant overview and unit-by-unit performance tracking, real-time economic evaluation, alerting functions, support to the planning department, operator skills management features, and so on • Use real-time data analysis to communicate with end-users •Offer tailored calculations, solutions, and optimisation options, including the ability to monitor the performance of closed-loop advanced process controls (APCs) and correct/ readjust such APCs as needed • Regard and handle digital monitoring as a requisite for the technologies they supply, as essential as online analysers, flowmeters, and thermocouples. Figure 1 shows an example of a modern aromatic complex configuration. A naphtha stream is hydrotreated and split into light naphtha (LN) and heavy naphtha (HN) streams, with the latter feeding a continuous catalytic reformer (CCR). Benzene and toluene are extracted from the light reformate, while the non-aromatics raffinate is typically directed to the refinery gasoline pool or used as steam cracking feed or fuel. The heavy reformate is sent to a xylene column where C 8 aromatics are collected overhead and feed a xylene loop for paraxylene separation, ethylbenzene conversion, and isomeri- sation of para-depleted xylenes. C 9 + aromatics are collected at the xylene column bottoms and further fractionated in a heavy aromatics column. C 9 and C 10 aromatics are recovered overhead of the heavy aromatics column and processed with toluene in a transalkylation unit to produce additional benzene and xylenes, while C 11 + aromatics are collected at the heavy aromatics column bottoms and typically exported as fuel oil. Aromatics complex: interconnected processes The aromatic complex constitutes a textbook case of inter- connected processes. Below are a few examples of process changes influencing other processes: • The naphtha splitter operation can be adjusted to retrieve more molecules from the LN stream and direct them via the
HN stream to the CCR for additional benzene production. The CCR operation needs to be fine-tuned accordingly, not only to maximise aromatic products but also to minimise concurrent olefins make; further, the reformate splitter, as well as the extraction process, need to be able to accom- modate such additional benzene product. • Paraxylene adsorption utilisation at near full capacity heav- ily depends on the proper operation of the upstream clay treater and/or selective hydrogenation unit. 3 Meeting olefins specification is critical for the C 8 aromatics stream feeding the paraxylene selective adsorption unit, and thus real-time management of potential upsets in the olefins removal pro - cess is essential in modern aromatic plants. • In facilities where xylenes isomerisation is split between gas phase and liquid phase processes, energy consumption is minimised when traffic through the liquid phase process is maximised. However, xylene loop ethylbenzene concentra - tion increases with traffic through the liquid phase process, which reduces the overall loop efficiency. Consequently, energy consumption reduction, ethylbenzene conversion, and overall loop efficiency require real-time optimisation. • The heavy aromatics column operation sets the nature and quantity of C 9 , C 10 and possibly C 11 aromatics that will feed the transalkylation process to produce additional xylenes and benzene. More heavy aromatics in the feed typically means higher xylenes production per pass and a higher ageing rate for the catalyst used in the transalkylation unit. Higher xylenes and lower benzene production or vice versa impact paraxylene recovery as well as benzene fractionation. • Feed selection affects all units in the aromatics plant. The use of a process operating simulator (POS) for pre-screen- ing of available naphtha streams allows overall complex simulation and prediction of product slate associated with different feed scenarios. The POS is an essential planning tool for fast decision making to respond to PX production demand as needed. Real-time digital performance monitoring cases Nowadays, data densification techniques such as machine learning enable the creation of soft sensors to compensate for lab analyses low frequency. Consequently, it is possible to determine – with the same granularity as based on actual
Navigation
Distillation Column Optimisation
Messenger
Alerts
Reconciled MB
Aromatic Yeild Maximisation
Columns Overview
Instantaneous Dashboard
Alerting
Reconciled Yields
Debutaniser
Optimised Unit Performance
Normalisation
Depentaniser
Optimiser Status
Normalised PKIs
SEND
General Performance
Unit Capacity Usage
Reactor WAIT
Material Balance
ExReactor Yield : H2
ExReactor Yield : C5+
ExReactor Yield : Arom...
ExReactor : Aromatics i...
Reformate RON
wt%
˚C
wt%
wt%
wt%
wt%
wt%
Figure 2 CCR in operation
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Daily Recommendations (-24H)
Recommended Process Conditions (-24H)
Expected Performance Improvement (-24H)
WAIT Change [˚C]
H2/HC Ratio Change [mol fr]
Catalyst Circulation Rate Change [...
Aromatic Yeild Delta [wt%]
24H Aromatic Production Delta (wt. basis) [ton]
Aromatic Yield Delta (wt. basis)
Aromatic Yield Delta (wt. basis)
wt%
kg/h
wt%
kg/h
wt%
kg/h
wt%
wt%
kg/h
wt%
kg/h
wt%
avg current
avg current
Aromatic Production Delta (wt%)
Aromatic Yield Delta (wt%)
Optimised Process Parameters Over Selected Time Period (indicated on the top right)
Aromatic Yield : Optimised vs. Operation
Delta WAIT vs. Delta Aromatic Yield
wt%
˚C
wt%
wt%
˚C
wt%
wt%
˚C
wt%
wt%
˚C
wt%
wt%
˚C
wt%
wt%
˚C
wt%
wt%
˚C
wt%
avg
current
avg
current
ExReactor Yield: Aromatics (wt%) (calc.) Optomised Aromatic Yield
Delta WAIT Delta Aromatic Yield [Optimised - Measured] (right-y)
Figure 3 Optimiser targeting maximum aromatics production
process data – the compositions required for near real-time modelling. The following cases are from aromatic complexes currently in operation. For example, in the naphtha splitter, LN detailed carbon breakdown can be continuously determined to estimate the total naphtha feed TBP curve based on carbon PONA composition. The splitter LN/HN operating cut point can be calculated, and a set of optimal conditions is proposed in real-time to operators in order to adjust the LN swing cut flow rates that can be directed to HN. Aromatics are increased by maximising benzene precur- sors in the CCR unit inlet while checking continuously for potential bottlenecks in downstream units such as the aro- matic extraction unit. By applying this strategy in a 1 Mtpy paraxylene production complex, the achievable margin gain is estimated to be ~$4M/year, based on the incremental value of LN conversion to benzene vs LN conversion to liq- uefied petroleum gas (LPG). Figure 2 shows the real-time status of an operating CCR. Actual figures have been masked for proprietary reasons, but the process performance overview includes unit capac- ity usage, reactor temperature and pressure, hydrogen production, aromatics yield, FG/LPG ratio, material balance calculation, and coke on spent catalyst, among many other units of measure. Alert functions attract the viewer’s attention to operation
within design specifications (in green), outside design speci - fications but within operating constraints (in orange), or out - side operating constraints (in red). Each parameter can be viewed as a function of time – graph mode – or in an ‘odome- ter view’ mode. A separate screen displays the impact of the CCR operating conditions on the reformate splitter opera- tion. A ‘what if’ tool calculates performance prediction as a function of a new set of operating parameters, allowing the operator to evaluate the impact of changes in feed composi- tion, temperature, pressure and so on the overall yields, cata- lyst consumption, and heater duty and performance. Reducing plant production costs and energy consumption is achieved by implementing monitoring for equipment such as heaters or feed effluent exchangers. For instance, alerts in case of consumption anomalies will be triggered on the dif- ferent dashboards, and calculated key performance indica- tors (KPIs) are pinpointing where it is possible to improve the efficiency of different assets while checking their integrity. Thanks to this permanent monitoring, traditional preven- tive maintenance can be reduced considerably and correc- tive maintenance almost eliminated, thereby significantly dropping associated costs. For example, tracking precisely excess air at the arch provides insights for heater efficiency optimisation; applied to CCR heaters, this enables an inlet process flow/total fuel gas flow ratio increase by a factor of +3%, decreasing CO 2 emissions accordingly.
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Norm NART
END OF CYCLE PROJECTION TOOL
Determine the remaining time for the Current Cycle. The deactivation slope/slope-uncertainty/Current NART/Target NART is editable by users
EDITABLE PARAMETERS
CALCULATION OUTPUTS Remaining Cycle Length Max Remaining Cycle Length Remaining Cycle Length Min
Deactivation Slope Deactivation Slope Uncertainty
Current NART Target NART
avg current
equation
TransPlus Average Reactor Temperature TransPlus Normalised Average Reactor Temperature
Regression TransPlus Normalised Average Reactor Temperature
y*
*x(months) + [
].r2-0.122
TransPlus Normalised Days on Oil (righty)
NART Projection What-if Tool
Cycle Prediction with NDOO
End of Cycle Projection
Run
Historical Data
Calculation Done
Min.Est.Cycle Length Est.Cycle Length Max.Est.Cycle length
WIT Name TP NART Projection
NART PROJECTION INPUTS
Description
Unit
Min
Max
Base Case
Deactivation Slope
˚C/mo
Deactivation Slope Uncertai...
%
Current NART
˚C
Normalised days on Oil (Months)
mo ˚C
Current NDOO Target NART
Cycle Production with Actual Date
NART PROJECTION INPUTS
Description
Unit
Min
Max
Base Case
End of Cycle Projection with Actual Date
Current NART
˚C
Target NART
˚C
Historical Data Min.Est.Cycle Length Est.Cycle Length Max.Est.Cycle length
Deactivation Slope Uncertai...
%
Deactivation Slope Min
˚C/mo
Figure 4 Transalkylation process in operation
Monitoring the main KPIs enables unit troubleshooting by continuous comparison of actual and normalised per- formances, calculated based on reconciled data using high fidelity models supplied by the process licensor or catalyst
supplier. In addition, an optimisation module targeting aro- matic yield maximisation can be set up (see Figure 3 ). The optimiser will only search within the defined upper and lower boundaries of the weight average inlet temperature,
Absorbers DP (Calculation)
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Absorber A (Calculation)
Absorber A (Measured)
Absorber B (Calculation)
Absorber B (Measured)
STEP
Absorbers DP (Calculation) 12 Steps Shift
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Absorber A (Calculation)
Absorber A (Measured)
Absorber B (Calc) 12 Steps Shift
Absorber B (Measured) 12 Steps Shift
STEP
Figure 5 Adsorption section in operation
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