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Updating industrial facilities to increase the level of automation and digitalization to match Industry 4.0 paradigms has become essential for many companies. Following such a trend, this paper presents a real-time optimization algorithm that plays a central role in a larger project framework devoted to highly interconnecting different network components of an Italian chemical industrial site. The proposed methodology aims at best managing the production rates of various products to fulfill a sales plan organized to satisfy numerous client requests. The considered model takes into account both batch and continuous processes as well as salable and non-storable products. The algorithm structure relies on the use of a non-linear optimization scheme and on the concepts of batch scheduling. Different features of the proposed methodology have been tested on real plant data, showing how the predicted forecast always improved the initial operation plan by considering both aspects of feasibility and economic nature. The use of the proposed algorithm assures the basis for fully integrating the control systems and the selling department of the facility in a more interactive and responsive manner.

The evolution of the process industry in the direction of automation and digitalization is nowadays a consolidated phenomenon. In this direction, Industry 4.0 paradigms are leading many industrial companies to significantly update their facilities. This paper presents a scheduling algorithm that takes the role of a real-time optimization (RTO) element in a larger project framework where the various network components are aimed to be all highly interconnected. The proposed methodology is applied to an Italian chemical industrial site, in order to best manage the production rates of the various products and the sales plan for the different clients. Numerous plants and processes are considered into the model: batch and continuous production lines, saleable and non-storable products. Concepts of linear optimization and batch operation scheduling are used in the algorithm construction. This whole structure lays the foundation for a full integration between different elements of the facility, that is, the control systems and the selling department.

Two supply chains for biomethane are here analyzed and modeled: gasification of short rotation forestry (SRF) poplar wood chips and anaerobic digestion of two species of microalgae, Chlorella vulgaris, and Nannochloropsis gaditana. Mass and energy balances are carried out along the whole systems, from the cultivation step to the stages of upgrading and injection into gas grid. A simulation model is employed to obtain gasification and upgrading parameters, energy requirements, and CO2emission. Harvesting of microalgae requires high inputs of energy and fertilizers, while syngas upgrading is more demanding than biogas treatment, as high volumes of water and CO2must be separated. Yields of the supply chains are very different: 7:1 biomethane:syngas ratio and 2:1 biomethane:biogas ratio have been estimated. Both of the supply chains could be optimized by using heat recovery methods. The highest removal of CO2and steam can be attained through absorption by triethylene and monoethanolamine solution, respectively.

A Computational Fluid Dynamic model of oxy-coal devolatilization experiments in a pilot-scale entrained flow reactor is proposed to gain insight into the thermal histories of the cloud of solid fuel particles and to investigate sources of uncertainties that may affect the analysis of devolatilization. Solid fuel particles experience different paths and are characterized by a temperature which is lower than the reactor nominal one at some of the sampling locations used for investigating devolatilization rates. Indeed, neglecting the quick heating up of the particles by assuming their temperature to be equal to the reactor one for calculating kinetics leads to an underestimation of devolatilization rates. A simple hypothesis on a linear dependence of the particle temperature on residence time through an average heating rate, with residence time and heating rate estimated from the numerical model, was found to largely improve the reliability of devolatilization kinetics even with a simple Single First Order Reaction model. An analysis of uncertainties on the kinetic parameters due to the cloud of particles is performed.

•Analysis of closed loop recycling effects on cathode materials properties.•Model validation against experimental data of a commercial battery cell.•Impact assessment of recycled cathode materials on battery performance.•Identification of compensatory electrode designs to overcome cathode limitations.•Negligible implications of compensatory electrode designs on battery specification.

Biomass fuels represent a renewable energy source, they are CO2 neutral fuels, and their use reduces the consumption of fossil fuels and limits the emissions of SOx, NOx, and heavy metals. They are used in pyrolysis, gasification, combustion, and co-combustion. The devolatilization is a fundamental mechanism in all these processes, especially for high volatile matter fuels. In this work, the devolatilization of biomass fuels (of different origin, properties, and composition) and biomass components is studied coupling thermogravimetric (TG) analysis with infrared spectroscopy. The characteristic temperatures are determined for the main devolatilization steps and compared for all fuels. A bituminous coal and a paper sludge are also studied for comparison. Light gases released (CO, CO2/CH2O, CH4, CH3OH, HCOOH) are detected, whereas more complex organic (hydrocarbon and oxygenated) compounds are grouped because of the large variety of volatile species released in a narrow range of temperature. The weight loss of biomass fuels is related to their chemical composition (i.e., considering the devolatilization behavior of cellulose, hemicellulose (xylan), and lignin in the same operating conditions). The aim of the work is to apply a summative law for the TG results (validated in previous experimental and literature works) to obtain the chemical composition of biomass fuels and to validate and extend a summative law for the FTIR profiles of volatile species released. Calculated values obtained using this method are in good agreement with the experimental results. Therefore, the validation of this correlation allows the prediction of the devolatilization of biomass fuels considering the initial chemical composition. This is useful for practical applications, plant designing, handling, and modeling.

The present contribution describes some of the activities carried out in the domain of combustion modelling at ENEL Research Generation Department. They concern the development and use of CRFD codes applied to the design of industrial furnaces. The objective is to predict the emission of pollutants, such as nitrogen oxides, in real combustion systems. A new approach has been developed, as a reasonable trade-off between engineering needs and computational resources limits analysing the 3D CRFD flow fields, an "equivalent" chemical reactors network model is extracted with corresponding residence times distributions and average local properties. Then a detailed kinetic calculation is performed on this simpler scheme. In this paper, a description of the CRFD codes is given, and the methodology to extract a chemical ideal reactors model from CRFD fields is presented. Finally, some relevant applications to industrial furnaces are discussed.