Plant-wide Optimization Research Articles

Distributed Optimization Subject to Inseparable Coupled Constraints: A Case Study on Plant-Wide Ethylene Process

Plant-wide optimization plays a vital role in improving the overall performance of large-scale industrial processes. Considering the modeling complexity and convergence difficulty of centralized plant-wide optimization, this paper proposes a distributed framework by decomposing the global optimization problem into a set of subproblems, where multiple local units interact with each other between nodes. According to the proposed framework, plant-wide optimization problem can be effectively solved by distributed optimization. To eliminate the limitations of existing distributed algorithms, we introduce constraint node to describe the inseparable coupled constraints between nodes. By combining Lagrange duality and parameter projection, the proposed algorithm can solve optimization problems with multiple constraints. Taking ethylene production process as an example, the global energy consumption optimization is guaranteed without the whole-process mechanism model. Numerical simulation and industrial experiment results demonstrate that the proposed algorithm can reduce the energy consumption of the entire ethylene process with fewer computation time.

A Self-Learning Based Dynamic Multi-Objective Evolutionary Algorithm for Resilient Scheduling Problems in Steelmaking Plants

Scheduling is one of the most important missions for plant-wide optimization in steelmaking manufacturing systems. In the context of dynamic scheduling, the decision-maker should simultaneously minimize economic objectives within the decision space and violation penalty out of the decision space. In this study, we introduce a resilient scheduling model in steelmaking plants, which provides flexible decisions, including buffering times in between stages and controllable processing speeds in the casting stage, to enable the solution to absorb random disturbances and recover quickly. We formulate the dynamic steelmaking scheduling problem with resilient responding strategies, which is a variant of dynamic multi-objective optimization problems (DMOP), and propose a resilient scheduling optimization framework to solve it over time. First, we employ a vector with problem-specific knowledge to map the whole decision space to sub-schedules in the casting stage, which contains casting priority, casting speed and scaling ratio. Next, we form a multi-objective linear programming model to evaluate these problem-specific vectors. Last but not least, we develop a self-learning based dynamic multi-objective differential evolutionary algorithm to solve the variant DMOP, in which a hypothesis-testing technique is used to detect and identify environmental changes. The sensitivity analysis and algorithm comparisons are performed on a wide range of problem instances under dynamic environments. Experimental evidence validates that the proposed resilient model and the optimization framework is effective to solve the dynamic scheduling problem in steelmaking plants. Note to Practitioners—This paper investigates a dynamic scheduling problem that comes from steelmaking manufacturing systems and is extensively studied in existing works. Because a decision-maker only has incomplete knowledge about the realistic environments, this paper simultaneously considers that both the decision and objective space of a scheduling problem dynamically changed over time. We develop a resilient scheduling model which can absorb the different types or scales of disturbances caused by unforeseen events, and recover rapidly to the original state. We formulate the resilience scheduling model and propose a dynamic multi-objective algorithm based on the self-learning differential evolutionary algorithm to solve it. The effectiveness of the proposed model and algorithm is validated via sensitivity analysis and comparison to other well-known algorithms. Furthermore, the resilient scheduling model and its optimization framework can also be applied to dynamic scheduling problems in other industries.

Analysis of the overall pressure balance loop for a methanol-to-olefins dual fluidized bed reactor-regenerator

Development of a comprehensive reactor model is of paramount importance for design and scale-up of methanol-to-olefins (MTO) process in a dual fluidized bed reactor (DFB). These models must integrate suitable reaction kinetic expressions with hydrodynamic models properly descriptive of gas-solid contact in fluidized bed reactors. In this modeling study, our previously developed kinetic models of MTO fluidized bed reactor and regenerator are coupled with overall mass, energy and pressure balances to ensure smooth circulation of catalyst particles between the two fluidized beds. This integrated model was then applied to determine geometric dimensions of a demo-scale MTO DFB configuration and to obtain the mass distribution of catalyst particles throughout the entire system including the pipes connecting the two reactors. Our model is capable of being integrated into simulation software such as Aspen Plus for plant-wide optimization and scale-up studies.

Industrial applications of digital twins.

A digital twin (DT) is classically defined as the virtual replica of a real-world product, system, being, communities, even cities that are continuously updated with data from its physical counterpart, as well as its environment. It bridges the virtual cyberspace with the physical entities and, as such, is considered to be the pillar of Industry 4.0 and the innovation backbone of the future. A DT is created and used throughout the whole life cycle of the entity it replicates, from cradle to grave, so to speak. This article focuses on the present state of the art of DTs, concentrating on the use of DTs in industry in the context of smart manufacturing, especially from the point of view of plantwide optimization. The main capabilities of DTs (mirroring, shadowing and threading) are discussed in this context. The article concludes with a perspective on the future. This article is part of the theme issue 'Towards symbiotic autonomous systems'.

Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4D, 5D, and 6D) are compared to a non-optimized 7D deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4D deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases.

A novel real-time optimization compensation method based on POPOA for the gold hydrometallurgy process

The gold hydrometallurgy process has been prevalent in the hydrometallurgical industry. With a further understanding of the process, the process mechanistic model has been constructed for performing control and optimization. However, due to random disturbances and changing operating conditions, the operating performance of the nominal model-based process has changed. To further perform real-time optimization of the gold hydrometallurgy process, a novel real-time optimization (RTO) compensation method based on the process operating performance optimality assessment (POPOA) is proposed in this paper. Firstly, the operating performance of the online process is judged by the process operating performance optimality assessment algorithm. For the case that the process operating performance is the optimal grade, in which the process operating state is close to the optimal state, but it is not in the optimal state due to some small disturbances in the process, the self-optimizing control (SOC) method is realized by taking the combinations of measurements as the controlled variables (CVs), which are tracked at the optimally insensitive set-points. Furthermore, under the condition of the operating performance is non-optimal grade due to the influence of serious disturbances or obvious change of operating conditions, a similarity detection procedure is complemented based on a SOC model base for searching the CVs set-points relative to similar models. When there is no usable set-point, it is necessary to perform the SOC offline modeling to update the model, or to find similar cases in the historical optimal operational case base to realize the compensation operation strategy based on just-in-time (JIT). Specifically, when similar samples cannot be found in the historical SOC base and the optimal operational case base, the plant-wide optimization model must be rebuilt to meet the requirements of the actual industrial production process. Finally, an industrial simulation experiment of the hydrometallurgical production process is carried out. The simulation results verify the effectiveness and practicability of the proposed new RTO compensation scheme.

Plantwide optimization coupled with column sequencing and stacking using a process simulator automation server

A method is proposed for plantwide process design and optimization that solves the column sequencing and stacking problems while also accounting for the tradeoff between reactor system design and separation system design. The plantwide process is represented by two flowsheets in a process simulator. One flowsheet represents the reactor network and the other flowsheet contains a single distillation column which is used to optimize each column in every sequence. When reactor design variables are specified, the reactor effluent flowrate and composition can be determined by simulating the reactor network flowsheet. Since the outlet from the reactor network is the feed to the separation system, the optimal separation system for this feed can then be designed. Finally, by looping over reactor design variables, a nearly-optimal plantwide process design can be determined. This permits plantwide optimization accounting for tradeoffs between the reactor section and separation section of a process while using rigorous modeling of both parts of the flowsheet. The rigorous models can still be constructed rapidly using commercial process simulation software. The method is illustrated with two case studies: the butane alkylation process and the cumene process.

Plantwide optimization via real-time optimization with persistent parameter adaptation

Determining the optimization scope is a major issue whenever implementing Real-time Optimization (RTO). Ideally, the optimization problem should encompass the whole plant and not a single unit, which represents only a local subset of the problem. However, if the standard RTO method, the two-step approach (TS), is applied to the entire plant, the whole system needs to be at steady-state (SS) in order to initiate the optimization cycle. This condition is rarely found in practice. One alternative is to apply Real-time Optimization with Persistent Parameter Adaptation (ROPA). ROPA is an RTO variant that integrates online estimators to the standard TS framework and avoids the need of waiting for steady-state to trigger the optimization cycle. However, the problem shifts to obtaining a dynamic model of the entire plant, which can be challenging and time consuming. This paper proposes a variant of ROPA, named asynchronous ROPA (asROPA), where the plant-wide model is partitioned into submodels and, depending on their characteristics, their parameters are updated using either online or steady-state estimators. Consequently, it is not necessary to obtain a dynamic model for the whole process. This asynchronous updating strategy allows the plant-wide model to be up-to-date to the process and the plant-wide optimization can be scheduled at any arbitrary time. The new strategy is applied to a case study consisting of a system whose model can be partitioned into a separation and a reaction submodel. The plant-wide results indicate that asROPA reacts much faster to the disturbances in comparison to the TS approach, improving the overall economic performance and is able to drive the system to the plant-wide optimum. Additionally, a strategy for partitioning the process and choosing the estimation strategy for each partition is proposed.

A Tchebycheff-based multi-objective combined with a PSO–SQP dynamic real-time optimization framework for cycling energy systems

The aim in this work is to develop an optimization framework to address cycling of baseload energy systems due to the penetration of renewables into the grid. The developed strategy corresponds to a multi-objective and dynamic real-time optimization (MOO-DRTO) framework applied to a postcombustion MEA-based CO2 capture process from a baseload coal-fired power plant under cycling conditions. A Tchebycheff-based method is used for the multi-objective optimization (MOO) component. Also, a hybrid approach consisting of Particle Swarm Optimization (PSO) and Sequential Quadratic Programming (SQP) is implemented for the first time in process systems engineering to solve the dynamic real-time optimization (DRTO) component. The objectives considered in the MOO-DRTO framework are economic and environmental. The proposed MOO-DRTO strategy is successfully implemented and 24-h optimal output trajectories for the carbon capture system under cycling are generated. Also, the optimal compromise is chosen from the Pareto front according to a set of selected weights for the objectives with minimal interaction between the framework and decision maker. The results indicated that the developed framework has potential to be extended to plant-wide optimization applications under cycling.

Deep Learning Approach for Industrial Process Improvement

The full operation of an industrial processing facility with artificial intelligence has been the holy grail of Industry 4.0. One of the inherent difficulties is the enumerate and complex nature of processing information within an industrial plant. Hence, such data should be processed efficiently. This paper demonstrates the effectiveness of a deep auto-encoder neural network for the dimensionality reduction of industrial processing data. The deep auto-encoder neural network functions to intake all possible processing data from the processing system by sending it into an encoder neural network. Subsequently, the encoder condenses the data into highly compressed encoded variables. The network is trained in an unsupervised manner, where a decoder neural network simultaneously attempts to revert the encoded variables to their original form. Such a deep learning approach allows data to be highly compressed into lower dimensions. The coded variables retain critical information of the processing system, allowing reconstruction of the full process data. Auto-encoder neural networks are also able to provide noise removal for encoded data. For application, the encoded variable can be utilized as an effective dimension-reduced variable that can be used for plant-wide optimization. This paper also discusses the further applications of encoded variables for industrial process improvements using the Industrial Internet of Things (IIoT) technologies.

Modeling and Testing of Temporal Dependency in the Failure of a Process System

The complexities of process plants are increasing because of process integration and plant-wide optimization. Failure models of a process system (henceforth referred to as process-accident models) ...

Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries

Integrated refineries and industrial processing plant in the real-world always face management and design difficulties to keep the processing operation lean and green. These challenges highlight the essentiality to improving product quality and yield without compromising environmental aspects. For various process system engineering application, traditional optimisation methodologies (i.e., pure mix-integer non-linear programming) can yield very precise global optimum solutions. However, for plant-wide optimisation, the generated solutions by such methods highly rely on the accuracy of the constructed model and often require an enumerate amount of process changes to be implemented in the real world. This paper solves this issue by using a special formulation of correlation-based principal component analysis (PCA) and Design of Experiment (DoE) methodologies to serve as statistical process optimisation for industrial refineries. The contribution of this work is that it provides an efficient framework for plant-wide optimisation based on plant operational data while not compromising on environmental impacts. Fundamentally, PCA is used to prioritise statistically significant process variables based on their respective contribution scores. The variables with high contribution score are then optimised by the experiment-based optimisation methodology. By doing so, the number of experiments run for process optimisation and process changes can be reduced by efficient prioritisation. Process cycle assessment ensures that no negative environmental impact is caused by the optimisation result. As a proof of concept, this framework is implemented in a real oil re-refining plant. The overall product yield was improved by 55.25% while overall product quality improved by 20.6%. Global Warming Potential (GWP) and Acidification Potential (AP) improved by 90.89% and 3.42% respectively.

Gold Recovery Modeling Based on Interval Prediction for a Gold Cyanidation Leaching Plant

The production index of gold cyanidation leaching process has an important influence on the economic benefits of the plant-wide hydrometallurgical process. In the actual leaching production process, due to the fluctuations of the previous procedure, the leaching process is often affected by the uncertainty and process disturbance. And hence, the prediction accuracy of the traditional point prediction models (such as ANN: Artificial Neural Network) decreases seriously and cannot provide any quantified information about the uncertainty or process disturbance. To solve the above problem, the interval prediction technique based on Radial Basis Function (RBF) ANN is proposed and used to model a gold cyanidation leaching plant encountering uncertainty and process disturbances in this paper. The objective function trained in the interval prediction model is not based on the prediction error, but the comprehensive measure index, namely, the coverage width criteria, which consists of the prediction interval coverage probability and the prediction interval normalized averaged width. Compared to the traditional point prediction, when the process uncertainty and disturbances are present, the interval prediction model will provide more helpful process information to the operators or process designers for determining further optimization and control strategies. The simulation and practical application results show that most of the real values of gold recovery can be covered between the upper and lower bounds with a predefined probability, which indicates the effectiveness and reliability of the interval prediction model, thus laying an important foundation for plant-wide optimization and control.

A new innovation paradigm: combining technological and social innovation

A new innovation paradigm is needed to answer the societal, economic and environmental challenges the world and companies are facing. The EU funded Horizon 2020 SPIRE Project “Coordinating Optimisation of Complex Industrial Processes” (COCOP) is combining technological and social innovation within a steel company pilot case (Sidenor). The project aims at reducing raw materials consumption (and energy and emissions reduction as well) by plant-wide optimisation of production processes based on a software solution and at the same time changing social practices. Key for COCOP is a methodology integrating technological innovation within a social innovation process of co-creation and co-development by involving (potential) users of the future software system and relevant stakeholders right from the beginning; thereby improving effectiveness and impact of the innovations and the implementation process. This involvement is instructed and measured by social key performance indicators (social KPIs) and operationalised in surveys (questionnaire and interviews) with future users, engineers and external experts (from different industry sectors not involved in the project). The article presents the results of the starting point of COCOP illustrating the future user perspective of the pilot steel company (Sidenor) contrasted by the view of external experts – seriously taking into account the interfaces between technology, human and organisation.

Real-time Optimization with persistent parameter adaptation using online parameter estimation

One of the major drawbacks of traditional Real-time Optimization (RTO) is the steady-state wait before estimating the parameters. This paper proposes an alternative solution called Real-time Optimization with Persistent Adaptation (ROPA), which integrates on-line parameter estimation in the optimization cycle, avoiding the SS detection step. Essentially, the idea is to use transient information to update the steady-state economic optimization problem and, then, by continuously solving it, the calculated optimal solution would reach the actual plant steady-state optimum in a given time horizon. ROPA provides an intermediary solution between static and dynamic optimization schemes. While it approximates the optimal trajectory, ROPA design enables the application of techniques to plant-wide optimization and the use of well-established static RTO commercial solutions. The new methodology benefits are illustrated with a case study, in which the traditional RTO and ROPA schemes are applied to the Williams–Otto reactor. Their performance is compared based on profit loss and deviation from the actual optimal decisions. The results show that the refinement of the prediction capacity by decreasing the time between two sequential optimization leads to a better economic performance and enhances the disturbance detection of the optimization cycle.

Towards Mineral Beneficiation Process Chain Intensification

As a part of the H2020 SPIRE project, Intensified by Design (IbD®), mineral beneficiation process chain will be intensified in terms of more efficient usage of energy and raw materials. The work focuses on optimizing control of the grinding circuit, coarse flotation process, new solids measurements and soft-sensors, as well as adaptive dynamic mass balancing. All demonstrators are interconnected through plant-wide optimization. In this paper, the process intensification activities carried out at the mini-pilot scale mineral beneficiation plant are briefly described. The real-time adaptation strategy utilizing Differential Evolution for parameter estimation in a sliding window is presented. The initial results show a successful parameter estimation result even in a short time window.

Model-based plantwide optimization of large scale lignocellulosic bioethanol plants

Second generation biorefineries transform lignocellulosic biomass into chemicals with higher added value following a conversion mechanism that consists of: pretreatment, enzymatic hydrolysis, fermentation and purification. The objective of this study is to identify the optimal operational point with respect to maximum economic profit of a large scale biorefinery plant using a systematic model-based plantwide optimization methodology. The following key process parameters are identified as decision variables: pretreatment temperature, enzyme dosage in enzymatic hydrolysis, and yeast loading per batch in fermentation. The plant is treated in an integrated manner taking into account the interactions and trade-offs between the conversion steps. A sensitivity and uncertainty analysis follows at the optimal solution considering both model and feed parameters. It is found that the optimal point is more sensitive to feedstock composition than to model parameters, and that the optimization supervisory layer as part of a plantwide automation system has the following benefits: (1) increases the economical profit, (2) flattens the objective function allowing a wider range of operation without negative impact on profit, and (3) reduces considerably the uncertainty on profit.

Multi-resolution model of an industrial hydrogen plant for plantwide operational optimization with non-uniform steam-methane reformer temperature field

Hydrogen is consumed in large quantities in the chemical industry. The most common industrial process for hydrogen production is steam-methane reforming, which is carried out using an energy-intensive furnace. The plant energy efficiency depends strongly on the spatial temperature distribution within the furnace; the narrower the distribution, the higher the efficiency that can be achieved. However, currently available studies on plantwide optimization of hydrogen plants ignore this crucial aspect. Adequate resolution of the spatial temperature distribution is necessary to determine the furnace operating temperature, which, in turn, determines the plant efficiency. In this work, a multi-resolution model of a hydrogen plant is developed. It includes a high-resolution model of the furnace, and low-resolution models, adequate for the purpose of plantwide optimization, of other unit operations. The developed model is used to determine the optimal process conditions after furnace temperature homogenization as part of a plant start-up or setpoint changeover procedure.

Plant-wide optimization and control of an industrial diesel hydro-processing plant

Diesel hydro-processing (DHP) is an important refinery process which removes the undesired sulfur from the oil feedstock followed by hydro-cracking and fractionation to obtain diesel with desired properties. The DHP plant operates with varying feed-stocks. Also, changing market conditions have significant effects on the diesel product specifications. In the presence of such a dynamic environment, the DHP plant has to run in the most profitable and safe way and satisfy the product requirements. In this study, we propose a hierarchical, cascaded model predictive control structure to be used for real-time optimization of an industrial DHP plant.

CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace

CCP-Based Plant-Wide Optimization and Application to the Walking-Beam-Type Reheating Furnace