Nucleophilic aromatic substitutions (SNAr) are key chemical transformations in pharmaceutical and agrochemical synthesis, yet their complex mechanisms (concerted or two-step) complicate kinetic model identification. Accurate kinetic models for SNAr are essential for scale-up, optimization, and control of the reaction process, but conventional methods struggle with mechanism uncertainty driven by substrates, nucleophiles, and reaction conditions, with data collection being difficult due to its source-intensive nature. We address this using DoE-SINDy, a data-driven framework for generative modelling without complete theoretical understanding. A benchmark study on the SNAr reaction of 2,4-difluoronitrobenzene with morpholine in ethanol was conducted, incorporating parallel and consecutive side-product formation. Ground-truth kinetic models validated in prior studies were used to generate in-silico data under varying noise levels and sampling intervals. DoE-SINDy successfully identified the true kinetic model with minimal runs, quantifying the impact of key design factors such as inlet concentrations, residence time, sample size and experimental budget on model identification.

Optimal energy scheduling for sector-coupled multi-energy systems is becoming increasingly important as renewable energies such as wind and photovoltaics continue to expand. They are very volatile and difficult to predict. This creates a deviation between generation and demand that can be compensated for by energy storage technologies. For these, rule-based control is well established in industry, and mixed-integer model predictive control (MPC) is an area of research that promises the best results, usually regarding minimal costs. Drawbacks of MPC include the need for an adequate system model, often associated with high modeling effort, high computational effort for larger prediction horizons, and complications with stochastic variables. In this work, Reinforcement Learning is used in an attempt to overcome these difficulties without applying elaborate mixed-integer linear programming. The self-learning algorithm, which requires no explicit knowledge of the system behavior, can learn a control policy and uncertainties of the variables just by interaction with the (simulated) system model. It is demonstrated that Reinforcement Learning (exchange factor = 36.8 %) can learn complex system behavior with comparable quality to model predictive control (ex. = 32.4 %) and outperforms rule-based control (ex. = 41.8 %). This is done in a case study with the goal of minimizing the exchange of energy with the grid, with a battery and hydrogen system providing storage flexibility. These results were achieved in the context that the Reinforcement Learning agent only has instantaneous rather than predictive information, i.e., a very limited state of information compared to the MPC. The trained policy is then deployed while significantly decreasing the computational effort.

The motivation of this work is an application of a production scheduling based on Real-Time Optimization and Zone Control Nonlinear Model Predictive Controller on a liquid recovery unit of an LPG production plant. In this unit, the scheduling-relevant disturbances occur on a time scale relevant to the system dynamics; thus, we propose a novel combination of a well-known control strategies leading to a hierarchical two-layered strategy, where the lower layer employs a zone control nonlinear model predictive controller (NMPC) to define inventory setpoints while the upper layer uses real-time optimization (RTO) to determine optimal plant-wide flow rates from an economic perspective. Unlike a traditional RTO, the proposed upper-layer problem is parameterized by product demands, with a distinct optimization problem formulated for each demand scenario. Our novel approach allows for proactive mitigation of potential inventory issues by dynamically recalculating the distribution of plant products. This approach addresses the challenges of storage bottlenecks and inventory fluctuations, and it is more effective than the typical operator decisions from an economic perspective in the system of interest.

Processes increasingly need to consider electricity markets, which shifts the traditional demand side management scope towards a more dynamic nature. Instead of only focusing on day-ahead energy trading, demand-side management scope should be broadened towards being able to support the power grid stability during frequency events. This paper studies an artificial example process, similar to the melt-shop process from the steel industry, highlighting the challenges and opportunities of an energy intensive process. We show the potential benefits of having a battery energy storage system on-site, as well as opportunities in lowering the electricity cost by participating in the bidding process of various ancillary products.

Reduction of greenhouse gas emissions is an important environmental element to actively combat the global warming and climate change. In view of reducing the CO2 concentration from the atmosphere, the Direct Air Capture (DAC) options are promising technologies in delivering negative carbon emissions. The integration of renewable-powered DAC systems with the CO2 utilization technologies can deliver both negative carbon emissions as well as reduced energy and economic penalties of overall decarbonized processes. This work evaluates the innovative energy- and cost-efficient potassium - calcium looping cycle as promising direct air capture technology integrated with various CO2 catalytic transformations into basic chemicals / energy carriers (e.g., synthetic natural gas, methanol etc.). The integrated system will be powered by renewable energy (in terms of both heat and electricity requirements). The investigated DAC concept is set to capture 1 Mt/y CO2 with about 75 % carbon capture rate. Up to 50 % fraction of the captured CO2 stream will be then catalytically converted into the Synthetic Natural Gas (SNG) or methanol using the green hydrogen produced by the water electrolysis (using renewable power), the rest being sent to geological storage. As the results show, the integrated DAC - CO2 utilization system, powered by renewable energy, has promising performances in terms of delivering negative carbon emissions, promoting the utilization of renewables and reducing ancillary plant energy consumptions.

A generally applicable framework for the evaluation of the sustainability of distillation processes is proposed by aligning indicators directly to selected sustainable development goals (SDGs) created by the United Nations. The indicators are related to the goals good health and well-being (SDG 3), clear water and sanitation (SDG 6), affordable and clean energy (SDG 7), decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9), responsible consumption and production (SDG 12), climate action (SDG 13) and life below water (SDG 14). A total of 12 sustainability indicators, including human toxicity potential, wastewater generation, water consumption, renewable energy share, energy demand, material footprint, profit, waste generation, recycling ratio of waste, greenhouse gas emission, eutrophication potential and acidification potential are assigned to selected SDGs. The application of the indicators is illustrated by two case studies: a batch (BD) and a continuous (pressure-swing) distillation process. Case BD6, where the CO2 emission was minimized, proves to be the most sustainable BD process. It has the lowest greenhouse gas emission, acidification potential and waste generation. Although it has the lowest profit, the variations across the BD cases are minimal. By the pressure-swing distillation, L4 (feeding into low-pressure column, heat integration with optimization) is the best case, with the highest profit and the lowest energy demand, material footprint and greenhouse gas emission values.

Reactive crystallization (RC) is a chemical process in which the reaction yields a crystalline product. It is used in various industries such as pharmaceutical manufacturing or water purification. In some cases, RC is the only feasible process pathway, such as the precipitation of certain ionic solids from solution. In other cases, a reaction can become a RC by changing the reaction environment to a solvent with low product-solubility. Despite the utility and prevalence of RC, it is not often emphasized in process design software. There are RC models that simulate the inner reactions and dynamics of a RC, but each has limiting assumptions, and are difficult to integrate with the rest of a process-line simulation. This modeling gap complicates RC process design and limits the exploration of the possible benefits to using RC as well as the ability to optimize a system that relies on it. To fill this gap, we built an open-source, customizable model that can be integrated with other unit operations in the Python process simulator package PharmaPy. This model focuses on the reaction-crystallization interactions and dynamics to predict reaction yield and crystal critical quality attributes given inlet streams and reactor conditions. In this way, RC can be integrated with other unit operations to capture the effects RC has on the process overall. The model and assumptions are described in this work. The model space, limitations, and capabilities are explored. Finally, the potential benefits of the RC system are shown using three example cases.

The growing concern over the environmental impacts of the wine industry has driven the search for sustainable technologies to manage its waste, particularly the residual sludge generated during effluent treatment. This sludge, rich in organic matter, represents a significant source of pollution if not properly treated. However, their energy content allows them to turn this environmental liability into an asset through innovative valorization. Hydrothermal liquefaction (HTL) emerges as a promising technology in this context. This process allows the direct conversion of residual sludge into high-energy-value liquid biofuels. Unlike other treatment methods, HTL can process wet biomass without needing prior drying, making it particularly suitable for managing sludge from wine effluents. Thus, this research aims to evaluate the conversion of residual sludge derived from wine effluent treatment into biofuels through a hydrothermal liquefaction simulation, integrating this process into a sustainable biorefinery for levulinic acid and bioethanol production. The methodology considers the determination of the composition and treatment of the wine effluent to define a case study. The biorefinery processes, as well as the HTL, are designed and simulated in Aspen Plus V.14, and also they are evaluated technically and economically. As a result, levulinic acid, sustainable aviation fuel, bioethanol, propylene glycol, and electrical energy are produced. In addition, the biorefinery reduces the effluent's Chemical Oxygen Demand (COD) by 99%. In conclusion, valorizing suspended solids from wine effluents through HTL is technically and economically feasible.

The performance of chemical plants is inevitably related to knowledge about the current state of the system. However, both process and equipment drifts may distort state information. Deviations of process values caused by equipment malfunction may be misinterpreted as process drifts and vice versa. Determining the cause of the drift is further complicated by the fact that equipment drifts typically occur in combination with process drifts. This paper presents a method that uses available additional equipment data to reliably detect and decouple combined equipment and process drifts in chemical plants by combining statistical methods with model-based approaches. The utility of additional equipment information is assessed based on its effect on the decoupling of process and equipment drifts. First results demonstrate the feasibility of the approach in a real plant.

Modern power grids coordinate electricity production and consumption via multi-scale wholesale energy markets. Historically, levelized cost metrics were the de facto standard for techno-economic analyses of energy systems and comparison of technology options. However, these metrics neglect the complexity of energy infrastructure including the time-varying value of electricity. An emerging alternative is multi-period optimization, which considers the locational marginal price of electricity as input data (parameters). In this work, we present a general interface for multi-period optimization with time-varying energy prices to facilitate rapid analysis and comparison of potential energy systems models. The PriceTakerModel class is written in the IDAES�-PSE platform and allows users to generate a multi-period, price-taker model instance, as well as automatically generate common operational constraints for their model, such as start-up and shutdown. We show this interface successfully generates multi-period price-taker models, facilitates model discrimination, and aids in analyzing various technologies for deployment in unique energy markets.