Online monitoring of semi-continuous emulsion copolymerization: Comparing constrained extended Kalman filtering to feed-forward calorimetry

Semi-continuous emulsion copolymerization (SCEP) is one of the major processes for the production of polymer latexes. For tailor-made copolymers, SCEP is employed because better control of polymer quality is possible compared to batch operation. To efficiently produce high quality polymers, recipe optimization and good feedback process control is required. For optimization or control purposes it is necessary to measure important product qualities such as the composition of the copolymer or the average chain length of the macromolecules. Measurements of these properties for this multi-phase process are expensive and usually not available online. State estimation and reaction calorimetry are possibilities to overcome this problem. Starting from a complex model for the considered process we develop a simplified model which enables us to estimate the concentrations of the monomers in the particle phase

Synthesis of separation processes with reinforcement learning

This paper shows the implementation of reinforcement learning (RL) in commercial flowsheet simulator software (Aspen Plus V12) for designing and optimising a distillation sequence. The aim of the SAC agent was to separate a hydrocarbon mixture in its individual components by utilising distillation. While doing so it tries to maximise the profit produced by the distillation sequence. All actions of the agent were set by the SAC agent in Python and communicated in Aspen Plus via an API. Here the distillation column was simulated by use of the build-in RADFRAC column. With this a connection was established for data transfer between Python and Aspen and the agent succeeded to show learning behaviour, while increasing profit. Although results were generated, the use of Aspen was slow (190 hours) and Aspen was found unsuitable for parallelisation. This makes that Aspen is incompatible for solving RL problems. Code and thesis are available at https://github.com/lollcat/Aspen-R

MINLP-Optimierung des Hybridverfahrens Destillation/Schmelzkristallisation

The hybrid separation process distillation/melt crystallization offers a cost wise attractive alternative compared to conventional distillation processes. In this work, the systematic and cost optimal design of this combination process is considered. The focus is on the optimal choice of operating and structural degrees of freedom

Dynamic modeling and control of a simulated carbon capture process for sustainable power‐to‐X

The goal of this study is to develop a dynamic model for a Carbon Capture (CC) process that can be integrated with a water electrolysis facility. The possibility of operating the post-combustion CC plant dynamically is investigated. The final model successfully tracks the parallel hydrogen production, providing the stoichiometric required CO2 stream for the subsequent methanol reactor. A dynamic model is used to configure controllers and to test the unit performance and stream conditions for various set points. Through the transient operation, the required feed gas is provided while optimizing the solvent and energy requirements. It is found that the slowest acting stage is the reboiler with a time constant of 3.8 h. Other process variables stabilize much quicker, requiring only a few minutes to reach steady‐state conditions. The hydrogen‐tracking scenario shows that the carbon capture plant can successfully operate under varying conditions with a maximum CO2 output increase of 7% of the minimum flowrate in the representative 24 h simulation time. The output CO2 stream is maintained at the desired >98% purity, 25 °C temperature, and 1.85 bar pressure, which allows to successfully perform hydrogen tracking operations

Analysis of the Potential of Meeting the EU’s Sustainable Aviation Fuel Targets in 2030 and 2050

Sustainable aviation fuel (SAF) is anticipated to have a significant impact on decarbonizing the aviation industry owing to its ability to be seamlessly incorporated into the current aviation infrastructure. This paper analyzes the potential of meeting the proposed SAF targets set by the ReFuelEU initiative. The approved SAF production pathways according to ASTM D7566 using renewable bio-based feedstocks were defined and analyzed. Moreover, a detailed matrix for comparison was used to provide an overview of the current state of those pathways. The analysis has shown that hydroprocessed esters of fatty acids (HEFA), alcohol to jet (ATJ), and Fischer–Tropsch (FT-SPK) are the most promising pathways in the foreseeable future due to their high technology readiness and fuel levels. HEFA is the most mature and affordable pathway; therefore, it is expected to form the backbone of the industry and stimulate the market in the short term despite its low sustainability credentials, limited feedstock, and geopolitical implications. On the other hand, FT-SPK can utilize various feedstocks and has the lowest greenhouse gas emissions with around 7.7 to 12.2 gCO2e/MJ compared to the conventional jet fuel baseline of 89 gCO2e/MJ. Overall, the EU has enough sustainable feedstocks to meet the short-term SAF targets using the current technologies. In the long term, the reliability and availability of biomass feedstocks are expected to diminish, leading to a projected deficit of 1.35 Mt in SAF production from bio-based feedstocks. Consequently, a further policy framework is needed to divert more biomass from other sectors toward SAF production. Moreover, a significant investment in R&D is necessary to improve process efficiencies and push new technologies such as power-to-liquid toward commercial operation

Novel dynamic cleaning model for cyclic operation of biodiesel membrane reactors

A membrane reactor produces high-quality biodiesel by combining both reaction and separation in a single unit. However, the reactor has disadvantages such as high operating expense and reduced efficiency over time due to membrane fouling. To solve this issue, frequent cleaning with physical and chemical methods is required. Membrane cleaning contributes to the reactor’s operating cost to a large extent, including energy, chemicals and even production loss. Although there have been studies undertaken focusing on improving membrane cleaning, optimizing the performance of the membrane reactor in biodiesel production has received limited attention. In this work, a novel membrane cleaning model is presented and used to optimize the membrane reactor efficiency in terms of biodiesel yield and cleaning costs. The model captures the dynamic states of reversible and irreversible membrane fouling during the cleaning process. It is further used to evaluate the effects of backwashing and chemical cleaning on the membrane reactor. The results show that the number of backwashes during operation is a crucial factor to improve reactor productivity and reduce the cleaning cost. The membrane reactor’s operating time rose 2 to 3 times when the operating period between two backwashes, or an operating cycle, reduced from 70 min to 15 min. The biodiesel yield increased significantly due to the extended operation. However, longer operating time led to an accumulation of more irreversible fouling, which could not be removed by backwashing. The cost of chemical cleaning rose as the irreversible fouling level increased. Regarding the cost-to-yield ratio of the biodiesel reactor, the best operating conditions were found at the operating cycle of 25 minutes between 2 backwashes. Overall, the cleaning model allows the prediction and reduction of the cleaning expense of the membrane reactor, increasing its potential as a biodiesel production technology significantly

Superstructure optimization for sustainable design of an algae biorefinery

In this study a superstructure of an algae biorefinery to produce added value products (pigments, omega-3, glycerol, biodiesel, biogas, and fertilizer) from microalgae is developed. From the superstructure optimization follows a cost optimal production pathway that consists of an open pond, sedimentation and flotation, flocculation/ centrifugation without a dryer, hydrothermal liquefaction, organic solvent pigment extraction, N-butanol lipid extraction, lipid production, and anaerobic digestion. The profits of the algae biorefinery depend on the types of wastewaters. 107 million Euros income can be earned annually using 0.2 million tons of influent wastewater. The total profit of an algae biorefinery that uses influent wastewater as feedstock is approximately two times higher than the wastewater of wheat straw biorefinery. 63 million Euros income can be earned annually using 0.6 million tons of influent wastewater. Furthermore, the total profits of algae biorefinery in each season are calculated by using real data of sunlight periods of the Netherlands. Summer is the best season with more than 36 and 22 million Euros profits for algae biorefineries when using influent wastewater and wastewater of wheat straw biorefinery, respectively. Calculating total profits of algae biorefinery by considering fix value for durations of sunlight has more than 30% error

Sustainable process technology to extract biochemicals from microalgae: A mini-review

In this work, we propose a mapping of suitable feedstock, products, and technologies used in various stages of algae biorefinery, including cultivation, harvesting, dewatering, drying, cell disruption, and extraction. The current technologies at each interval are studied and compared with each other. To set up, compare and select the most promising production pathways, a superstructure is proposed. A superstructure of algae biorefinery based on a literature review is developed and presented. Due to the significant impact of the environment on compositions and the number of microalgae, the effect of two factors i.e., light intensity and nutrient composition, on biomass production efficiency are critical. We will further demonstrate the potential application of algae as source for various products such as cosmetic ingredients, nutraceuticals (e.g., carotenoids and proteins), energy carriers (e.g., biodiesel, bioethanol, biohydrogen, biogas) and fertilizer