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This paper describes a collaborative digital twin approach for equipment dimensioning and selection in industrial process plants. Dynamic process simulator (Apros) was used to model the process and its automation, including pumps, while a product specific dynamic simulator (Virtual Drive) was used to model the motor and frequency converter. This approach allows all stakeholders to design and dimension the process equipment together in a holistic and energy optimal way. Simulation can be used to reach an optimal equipment solution that prevents overdimensioning, leading to up-front and total life-cycle cost savings. Co-simulation was made possible by implementing a prototype Functional Mock-up Interface (FMI) for both Apros 6 and Virtual Drive, allowing Apros to import Virtual Drive as a Functional Mock-up Unit (FMU). This paper shows how the FMI solution can be used for finding energy optimal selections for pumps and related powertrain products.

Ammonia is one of the most commonly used feedstock chemicals globally. Therefore, decarbonisation of ammonia production is of high relevance towards achieving a carbon neutral energy system. This study investigates the global potential of green ammonia production from semi-flexible ammonia plants utilising a cost-optimised configuration of hybrid PV-wind power plants, as well as conversion and balancing technologies. The global weather data used is on an hourly time scale and 0.45° × 0.45° spatial resolution. The results show that, by 2030, solar PV would be the dominating electricity generation technology in most parts of the world, and the role of batteries would be limited, while no significant role is found for hydrogen-fuelled gas turbines. Green ammonia could be generated at the best sites in the world for a cost range of 440–630, 345–420, 300–330 and 260–290 €/tNH3 in 2020, 2030, 2040 and 2050, respectively, for a weighted average capital cost of 7%. Comparing this to the decade-average fossil-based ammonia cost of 300–350 €/t, green ammonia could become cost-competitive in niche markets by 2030, and substitute fossil-based ammonia globally at current cost levels. A possible cost decline of natural gas and consequently fossil-based ammonia could be fully neutralised by greenhouse gas emissions cost of about 75 €/tCO2 by 2040. By 2040, green ammonia in China would be lower in cost than ammonia from new coal-based plants, even at the lowest coal prices and no greenhouse gas emissions cost. The difference in green ammonia production at the least-cost sites in the world's nine major regions is less than 50 €/tNH3 by 2040. Thus, ammonia shipping cost could limit intercontinental trading and favour local or regional production beyond 2040.

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As the share of renewable energy sources increases the need for energy storages increases also due to fluctuating nature of renewables (solar and wind). Power-to-Gas (PtG) is one a promising energy storing concept. In PtG process renewable electric energy is used to produce hydrogen via electrolysis. Hydrogen is used together with carbon dioxide in methanation process to produce storable methane. Automation and operation logics of PtG plant have been in the minority in the literature. In this work we have studied start-up and shutdown logics of a PtG plant with dynamic simmulations. With this approach we have identified development needs for such logics.

A constrained thermodynamic model was developed in order to describe the product gas composition of a methanation process. The non-equilibrium affinities of CO2 and CO methanation reactions were applied as additional constraints to the chemical system while solving the local thermodynamic equilibrium. The affinities of these reactions were modelled as a linear temperature dependent function based on published and experimental data. The applicability of modelling methodology was validated against two independent literature datasets and against our own experiments. In all cases, the model predicted the product gas composition of main species CO, CO2, H2, H2O and CH4. In two cases, it was sufficient to add only a single constraint describing the affinity of CO2 methanation reaction and in one case, two constraints describing the affinities of CO and CO2 reactions were needed. Results suggest that the modelling methodology presented can be utilised to describe different kinds of methanation process conditions, but the affinity models need to be defined based on the experimental reaction kinetic data. This methodology and the developed 0D-reactor model is applicable, for example, as part of a larger process simulation where additional details of methanation process are needed.

Dynamic process simulators for plant-wide process simulation and multiobjective optimization tools can be used by industries as a means to cut costs and enhance profitability. Specifically, dynamic process simulators are useful in the process plant design phase, as they provide several benefits such as savings in time and costs. On the other hand, multiobjective optimization tools are useful in obtaining the best possible process designs when multiple conflicting objectives are to be optimized simultaneously. Here we concentrate on interactive multiobjective optimization. When multiobjective optimization methods are used in process design, they need an access to dynamic process simulators, hence it is desirable for them to coexist on the same software platform. However, such a co-existence is not common. Hence, users need to couple multiobjective optimization software and simulators, which may not be trivial. In this paper, we consider APROS, a dynamic process simulator and couple it with IND-NIMBUS, an interactive multiobjective optimization software. Specifically, we: (a) study the coupling of interactive multiobjective optimization with a dynamic process simulator; (b) bring out the importance of utilizing interactive multiobjective optimization; (c) propose an augmented interactive multiobjective optimization algorithm; and (d) apply an APROS-NIMBUS coupling for solving a dynamic optimization problem in a two-stage separation process.

The purpose of the study was to investigate whether global sensitivity analysis can be utilised in concurrent process and control design to gain insight into the process and its control. This paper addresses the issue of sensitivity of a control law performance to its parameters in a dynamic, hybrid deterministic–stochastic process model. The control law under investigation is a collection of single-input, single-output type tower level controllers in a papermaking process. Global sensitivity analysis is shown to attribute higher importance to certain key parameters, thus providing valuable insight for the designer.

District heating grids are complex systems where energy production has to match the consumption load while key system parameters like temperatures and pressures through the grid have to be kept within limits. The choice of a control strategy for the grid depends on the selected key performance indicators. The scientific contribution of this paper is a methodology for controlling the supply water temperature setpoint of a heat power plant using heat consumption predictions. The proposed algorithm aims to provide more heat energy to the difficult consumers when they need it the most. The required input information are the short term weather forecast, the supply hot water temperature propagation delays of the district heating grid as a function of the grid load level and consumption profiles based on historical data or heat consumption models. The methodology is applied on a simplified case study of a 120MW district heating grid. The results showed that within a specific supply water temperature range the performance of the grid in terms of minimum pressure difference at the consumers over a year was significantly better using the proposed proactive algorithm compared to simple reactive and constant temperature control strategies.

Industrial applications with reliable predictive features are becoming increasingly important. A tracking simulator is an example of an online simulation system with great capabilities that fills the gap left by other predictive applications. In a tracking simulator, a simulation model is run in parallel with a physical process controlled by the process' control system. At the same time, a tracking mechanism is used to keep the state of the simulation model as close as possible to the real process by continually adjusting parameters of the model. The selection of these parameters impacts directly on the quality of the tracking simulation results and it is a complex task in processes with a big number of variables. This paper presents two case studies of tracking simulation where the controlled parameters are selected using different techniques. The first case study deals with a laboratory-scale hot water generation process where the parameters' selection is performed manually. The second case study deals with a combined heat and power production process with major uncertainties in the process structure. In this case, we focus on the variance decomposition method used to determine the most suitable controlled parameters. Conclusions and future work are finally presented.

The purpose of this paper is to present a method for assessing whether optimized process design and operations, based on simplified simulation models, can be carried towards real life. The proposed method relies on a more realistic, or if available, a set of more and more realistic simulation models being compared with the original model utilized in the optimization problem. The models which are utilized are dynamic and stochastic in nature, necessitating statistical analyses of the results. The method is applied recursively to a generic papermaking process model with the addition of increasingly more realistic modifications. It is shown how the proposed comparison method detects the effects of such modifications and thus provides information on how well the optimization results are applicable to real life situations. The purpose of the presented method is to add one element to an overall framework of simulation-aided process design.

Dynamic modeling and simulation software can be used to simulate the industrial processes such as papermaking in order to improve the efficiency of the process. A dynamic simulation model can be combined with an optimization method to maximize the advantages which simulation tools can offer. In this paper, we combine dynamic process simulation and optimization to see how it can benefit a papermaking process which was envisaged to consist of a TMP mill, stock preparation, short circulation, a paper machine, and a reel. The aim is to increase the efficiency and minimize the costs involved in papermaking.

A dynamic simulation model of a 3-layer board machine was developed. The simulation model consists of stock preparation and proportioning, short circulations, wire and press sections, and drying section including the steam and condensate systems. Physical, first principles models were used whenever possible. The control system and automatic grade change program were modelled as well. The simulator was extensively validated against measurements. Two simulation cases concerning grade changes are discussed: re-tuning of the automatic grade change program, and studying the potential of a new dryer.

Virtual commissioning of chemical plants often involves a dynamic simulator and an optimization method. This paper demonstrates the integration of APROS, a dynamic process simulator and IND-NIMBUS, an interactive multiobjective optimization software. We implement a multiobjective concentration control problem in APROS involving conflicting objectives and employ a decision maker to interact with IND-NIMBUS and express his preference information to finally obtain his most preferred solution. The results of this study show that APROS and IND-NIMBUS can be integrated and an interactive multiobjective optimization method can help the decision maker in exploring trade-offs among conflicting objectives and available solutions, and finally choose one solution as his preferred one.

The purpose of this work is to show that global sensitivity analysis combined with a process simulator can be used as a method of identifying process bottlenecks in the papermaking context. A variancedecomposition based Sobol' analysis is employed in three case studies with Aprosr Paper simulator. The results give evidence that the method is applicable in detecting process bottlenecks and can thus provide the process designer with useful information.