ORTO is part of Proserv's range of industry-leading controls technologies.Offering a self-learning and model-free approach to closed loop optimisation.
The self-learning system bypasses the need for complex, costly models, resulting in a much greater and faster return on investment. The agent based system makes it flexible and scalable as it uses skills that engineers already have.Using ORTO allows optimisation of power generation across all sectors.
Proserv ORTO™ real-time optimisationWhat could be gained by optimising operations in real-time?
ORTO use cases
Intial text to introduce the case studies.Vestibulum ornare risus quis orci commodo, eu rhoncus enim auctor. Ut vitae arcu sit amet libero ullamcorper finibus in nec turpis. Sed bibendum accumsan tortor sed congue. Nullam non felis a turpis pellentesque vehicula ut in nibh.Phasellus feugiat justo id ipsum tempor sodales. Donec sed ultricies tellus. Etiam diam tellus, convallis ut mauris sit amet, vestibulum sagittis nisi.Variable Speed Wind Turbine Power MaximizationPower generated by a wind turbine is a function of wind speed and air density. Wind speed, in particular, can vary significantly over a short period of time. As wind speed and air density varies, the optimum settings for tip speed ratio and pitch angle, i.e., settings which maximize the power generated, also vary.Traditional model-based RTO technologies are not suited to such applications as wind turbine dynamics are difficult to model.ORTO however is model free, continually adapting as conditions change. Wind speed and air density measurements are used to provide feedforward action, allowing ORTO to track changes in the optimum operating point.Read the full case study
Variable Speed Wind Turbine Power MaximizationBusiness ObjectiveTo maximize power generated by the turbine, between the cut-in and rated wind speeds and for any given air density. Prolonging the operating life may also be a key aim.Typical Optimization Objective FunctionMaximize power generated.By manipulating, within a permitted range:
Tip speed ratio (ratio of the turbine blades tip speed to wind speed)
SolutionTwo agents are required, to adjust tip speed ratio and blade pitch. Yaw angle (angle of turbine to wind direction) can remain under regulatory control, to keep blades at 90O to wind, unless yaw angle is used for wake steering optimization (see wind farm use case).BenefitsTypical a 2-3% increase in power generated, between the cut-in and rated wind speeds.
Wind Farm Power Maximization through Wake SteeringThe impact of wind turbine wake on downstream turbines is highly non-linear and difficult to model. Wind speed and direction can also change rapidly, causing the optimum operating points also to change rapidly. The optima will also be unconstrained. Traditional model-based RTO technologies are not suited to such applications.ORTO however is model free, continually adapting as conditions vary. ORTO can therefore track movements in the optimum operating point. Total power is thus continually being maximized, as prevailing weather conditions change.
ORTO agents are autonomous and highly scalable. They can be removed or added as needed, at any time, allowing for greater scheme flexibility. Wake steering optimization can therefore be applied incrementally across the wind farm over time, if desired. Read the full case study
Wind Farm Power Maximization through Wake SteeringBusiness ObjectiveA standalone wind turbine should be kept at 90O to the wind direction to maximise power generated. However, when placed in a wind farm, the resulting air turbulence caused by the rotating blades can adversely impact the power generated by downstream wind turbines. It may be advantageous to sacrifice some power generated by upstream wind turbines, by adjusting its yaw away from 90O, to reduce their impact on the downstream turbines, to maximise the total power generated by the wind farm. This is commonly referred to as wake steering optimization. Typical Optimization Objective FunctionMaximize total normalised power generated by an upstream and downstream wind turbine pair. Repeat for all pairs across the whole wind farm. Pairings will change as wind direction changes e.g., by >90O.By manipulating, within a permitted range:
Bias on yaw angle, on the upstream turbine.
Subject to suitable constraint limits on:
Power output on each turbine.
Mechanical integrity measurements e.g., stress, strain, vibration, on each turbine.
SolutionFor an NxM matrix of wind turbines, NxM agents are required, each adjusting yaw angle bias on its respective wind turbine. The bias is applied to the yaw angle regulatory controller setpoint i.e., SP = 90 ± bias. Wake steering optimization can be used in conjunction with single wind turbine optimization, which adjusts tip speed ratio and blade pitch to maximize power (see variable speed wind turbine use case).
Benefits
Typically, a 2-6% increase in power generated is possible, as wind speed varies between the cut-in and rated speeds. Due to a reduction in turbulence effects, the operating life of the wind farm can also be prolonged.
Optimization of an Anaerobic Digestion ReactorOptimization challenges:
The available savings are low relative to the implementation and maintenance costs
Process dynamics can be highly non-linear and hard to model
ORTO schemes are easy to design and implement, significantly reducing the time, cost and expertise needed. They also handle non-linearity implicitly, significantly reducing maintenance needs. Read the full case study
Optimization of an Anaerobic Digestion ReactorBusiness ObjectiveAnaerobic digestion is used to treat biodegradable waste and sewage sludge and as a source of renewable energy. The process produces a biogas consisting of mainly methane and carbon dioxide. This biogas can be used directly as fuel, in combined heat and power gas engines or upgraded to natural gas-quality biomethane. The nutrient-rich digestated sludge also produced can be used as fertilizer. Typical Optimization Objective FunctionMaximize biogas produced, per unit organic waste stream feed. By manipulating, within a permitted range:
Reactor heat input
Organic waste stream flow
Subject to the following constraints:
Digested sludge total organic carbon (TOC) content
Heat input limits
SolutionOn simple reactor designs, two ORTO agents should be sufficient.BenefitsTypically, a 3-5% increase in biogas.
Blending Optimization The impact of blend components on blended material qualities can be very non-linear. Furthermore, blend component properties vary over time and are often not known accurately. ORTO self-learning technologies, naturally handles non-linearity and variation in blend component properties ensuring the optimum is always found. The easy-to-use design significantly improves return on investment (by a factor or 2-5 times) and reduces the expertise needed. The technology is more scalable, enabling optimization to be applied to a small scope and then expand easily over time to capture increased savings. Read the full case study
Blending Optimization Business ObjectiveThe goal of gasoline and distillate in-rine blend optimization is to meet limiting product quality specifications at the lowest blend component cost. Typical Optimization Objective FunctionMaximize operating profit associated with blending (due to blend component costs) By manipulating:
Blend component flow rates or flow ratios
Subject to the following constraints:
Product quality limits
Blend system hydraulic limits
Click to zoomSolutionOn most blending operations, 3-5 agents will be sufficient. BenefitsThe typical benefit is a 2-5% reduction in higher cost blend components.
Optimization of a Diesel Hydrotreater Unit The hydrotreating process is non-linear and non-stationary i.e., process dynamics change over time as catalyst activity deteriorates. Traditional model-based RTO technologies are not suited to such applications as they require extensive ongoing upkeep to maintain performance. ORTO schemes however are easy to design and implement, significantly reducing the time, cost and expertise needed. Their self-learning nature implicitly handles non-linearity. Thus, sulfur content optimization is maintained without any ongoing upkeep burden. The technology is also very scalable, enabling the optimization scheme to be expanded easily over time to achieve increased benefits. Read the full case study
Optimization of a Diesel Hydrotreater UnitBusiness ObjectiveUltra-row sulfur diesel is produced using a hydrotreating unit. Sulfur is removed through reaction with hydrogen. To minimize operational costs, it is desirable to run the hydrotreater unit such that the final diesel product is at, or just below, the sulfur upper quality limit. Doing so prolongs catalyst life and potentially increases the unit’s ability to process heavier crudes for longer between unit turnarounds. Typical Optimization Objective FunctionProlong catalyst life by holding sulfur content in the final diesel product at or below a target e.g., 10 ppm. By manipulating, within a permitted range:
Fired heater process exit temperature
Reactor bed and / or feed temperatures
Hydrogen ratio to feed (dependant on regulatory control structure)
Subject to the following constraints:
Product sulfur quality high limit
Hydrogen supply valve opening limits
Hydrogen recycle compressor limits
Click to zoomSolutionOn a relatively simple hydrotreater unit, 3-4 agents will be sufficient. On larger more complex units, 5 or more agents may be required. BenefitsLife extension of catalyst. Ability to process heavier crudes for longer between turnarounds.
Production Maximization on an Oil & Gas Offshore Facility As reservoir pressures decline, oil and gas production reduces and, typically, the amount of produced water increases. Process dynamics associated with topsides equipment gradually change as a result and the operating position needed to maximize production moves. Traditional model-based RTO technologies are not suited to such applications. Process model mismatch increases with time and schemes require significant maintenance. ORTO however is model free, continually adapting as process conditions change. ORTO also senses when the optimum moves and tracks it over time. Production is therefore continually being maximized. Read the full case study
Production Maximization on an Oil & Gas Offshore Facility Business ObjectiveThe overriding business objective of any oil and gas production facility, is to maximise the oil and gas flowing from the reservoir, for any well configuration and within safe operating limits. Typical Optimization Objective FunctionOil and gas flow is increased by reducing the back pressure on wells. An optimization goal is therefore to minimize slug catcher pressure / HP separator pressure. By manipulating, within a permitted range:
Slug catcher / HP separator pressure
Choke valve position
Gas lift (if used)
Subject to suitable constraint limits on:
Compressor suction pressure / flow
Gas dehydration
Downstream oil and water handling
Slugging amplitude (if present)
Sanding (if present)
Click to zoomSolutionDepending on the process configuration, 2 or 3 agents should be sufficient. Agents can be added at any time, if further manipulated variables are identified. BenefitsTypical a 3-5% increase in oil & gas production. Such optimization can also increase total oil produced over the life of the reservoir.
Optimization of Reactors Optimization challenges:
Process dynamics can be highly non-linear
Process dynamics may be hard to model
ORTO self-learning optimization naturally handles non-linearity ensuring the optimum is always found. The easy-to-use design significantly improves return on investment and reduces the expertise needed. The technology is more scalable, enabling optimization to be applied to a small scope and then expand easily over time to capture increased savings. Read the full case study
Optimization of Reactors Business ObjectiveThe objective of reactor optimization is to find the most cost-effective operating condition, by balancing product yields, energy efficiency and throughput. Typical Optimization Objective FunctionMaximize operating profit which may be optimizing trade-off between energy efficiency, product yields and feed rate whilst pushing up to hydraulic and space velocity, heat transfer and quality limits. By manipulating:
Reactor temperatures
Additive reactant flows
Operating pressure
Subject to the following constraints:
Reactor temperature limits
Product quality limits
Reactor hydraulics or downstream hydraulic limits
Heat transfer limits (usually determined from valve positions)
SolutionOn small reactors units, 3 agents will be sufficient. On larger reaction systems more than 5 agents may be required. BenefitsThe minimum benefit will higher value product yields of between 2-5%. Energy efficiency savings of 3-10% may also be achieved.
Optimization of Separation Processes Optimization challenges:
The available savings are low relative to the implementation and maintenance costs
Process dynamics can be highly non-linear and hard to model
ORTO schemes are easy to design and implement, significantly reducing the time, cost and expertise needed. They also handle non-linearity implicitly, significantly reducing maintenance needs. Read the full case study
Optimization of Separation ProcessesBusiness ObjectiveSeparation units are commonly found on process plants. Examples of separation methods include distillation, solvent extraction, filtration and floatation. In most instances there is a trade-off between energy use and separation efficiency. Maximum operating profit is usually achieved by minimizing energy used to deliver the desired purity of separation. Typical Optimization Objective FunctionMinimize energy used, per unit feed. By manipulating, within a permitted range:
Energy input e.g., steam to a reboiler
Flow rates within the unit e.g., reflux flow
Operating pressure
Subject to the following constraints:
Product quality limits
Equipment hydraulics, e.g., ΔP to infer column flooding limits
Heat transfer limits e.g., steam valve position
Click to zoomSolutionOn small separation units, 3-5 agents will be sufficient. On larger multi-product separation processes more than 10 agents may be required. BenefitsThe minimum benefit will usually be a 3-10% reduction in energy use. In some cases, there can be yield benefits of up to 5%.
Optimization of Fired Heaters & Boilers The challenge with fired heaters and boilers is to allow for non-linearity and changes in process dynamics as operating demands vary. For some smaller systems (e.g., smaller packaged boilers), the return on investment is often insufficient to justify installing traditional real-time optimization technologies.ORTO schemes however are easy to design and implement, significantly reducing the time, cost and expertise needed. This means optimization can be economically justified on fired heaters and boilers, where only relatively small energy saving opportunities exist. The technology is also very scalable, enabling optimization to be applied to a small scope and then expanded easily over time to capture increased savings. Read the full case study
Optimization of Fired Heaters & BoilersBusiness ObjectiveMinimize energy use and manage the risk of sub-stoichiometric operation, when faced with changing operational needs. For example, changing steam demands, electricity generation, heated stream temperature, kiln clinker qualities etc. Typical Optimization Objective FunctionMinimize cost of combustion fuel, whilst meeting operating needs and ensuring equipment safety. By manipulating, within a permitted range:
Air to fuel ratio
Fuel ratios, if a range of fuels are used
Preheated air temperatures
Air flow splits to different regions of the fired equipment
Duty split between parallel equipment (e.g., preheat trains, parallel furnaces or boilers)
Subject to the following constraints:
Equipment temperature limits
Stack gas quality limits e.g., excess O2, CO
Exit temperatures and / or pressures
Click to zoomSolutionOn most fired heaters and boilers, 2 to 4 agents will be sufficient. On more complex systems up to10 or more agents may be required.BenefitsThe typical benefit is a 2-5% reduction in energy use.
Variable Speed Wind Turbine Power Maximization
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Wind Farm Power Maximization through Wake Steering
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Optimization of an Anaerobic Digestion Reactor
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Blending Optimization
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Optimization of a Diesel Hydrotreater Unit
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Production Maximization on an Oil & Gas Offshore Facility
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Optimization of Reactors
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Optimization of Separation Processes
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Optimization of Fired Heaters & Boilers
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Why ProServ
Real-time OptimisationLorem ipsum dolor sit asec, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.Enhanced ReliabilityLorem ipsum dolor sit asec, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.Increased Energy OutputLorem ipsum dolor sit asec, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
