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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.

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.

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.