Computers & Chemical Engineering最新文献

Methanol is one of the most important chemical compounds, as it is the basis for producing a wide variety of derivatives. Its production through fossil sources such as natural gas in countries like Mexico is not entirely viable due to the fluctuations in the availability of this resource. The use of renewable sources to produce methanol represents an interesting area of opportunity to reduce the dependence on a single raw material. This work proposes the design of the methanol supply chain in Mexico using residual materials, finding a solution with the best compromise between profit, social impact, and CO₂ emissions. The solution with the best compromise corresponds to a profit of 7,334,100 USD/y, a marginalization index of 2592.536 and CO₂ emissions of -0.021 Mt/y. This solution has 8 different types of raw materials, 18 process plants and the use of three processing technologies: gasification, anaerobic digestion, and catalysis from CO₂.

Fault detection and diagnosis (FDD) is an essential tool to ensure safety in chemical industries, and nowadays, many reconstruction-based deep learning methods are active in fault detection. However, many algorithms still suffer from not ideal actual performance. Inspired by the core mechanism of Transformer and large kernel convolution, this paper proposes a novel model combining variate-centric Transformer with large kernel temporal convolution. Variate-centric Transformer depends on self-attention to capture the multivariate correlations of input data, and large kernel temporal convolution collects period information to summarize temporal features. A benchmark dataset Tennessee Eastman process (TEP) and experiment data from the microreactor process are used to test the performance of fault detection. Compared with other reconstruction-based methods, results demonstrate that our model achieves a higher fault detection rate and a lower detection latency, and shows a significant potential for process safety.

This paper proposes a closed-loop system identification method based on hysteresis loop bias relay feedback without using excitation. The method utilizes hysteresis loop bias relay instead of a controller to ensure the informativity of data without external excitation and with only noisy feedback. Additionally, this paper proposes a parameter scheme for the hysteresis loop bias relay to guide the selection of parameters more rationally, thus improving the accuracy of the identification model. Compared to recently proposed switching controller methods, the advantage of this method lies in the fact that it requires fewer samples and shortens data collection time. The method's effectiveness is verified through simulation comparisons with typical methods.

Increasing supply and demand uncertainty, coupled with unforeseen disruptions, pose challenges to the resilience of today's critical sectors in the global food industry, including the broiler supply chain. This study introduces a resilient model to enhance the sustainability and resilience of the broiler supply chain in the face of uncertainties and disruptions. The model integrates backup facilities and employs multiple sourcing strategies to reinforce resilience. Using mixed integer linear programming with bi-objective, multi-period, and multi-product features, the model aims to minimize carbon dioxide ($C{O}_2$) emissions from transportation while maximizing overall supply chain profit. The goal programming, and the ε-constraint methods optimize decision-making and yield Pareto solutions, achieving a balanced approach to conflicting objectives. Also, robust optimization and stochastic programming provide practical solutions for handling uncertainties. Validation and sensitivity analysis confirm that the proposed model optimizes the broiler supply chain, enhancing resilience, sustainability, and profitability.

In this study, we tackle the problem of pharmaceutical supply chain optimization using a multi-objective model that simultaneously considers cost minimization, environmental impact minimization, and maximizing of service level equity (minimum ratio). This represents the three alms of sustainability which are key in manufacturing. Furthermore, we developed a disruption model capable of effectively managing disruptions within the supply chain and compared the capabilities with the baseline model.

The result shows how the supply chain network behaves under different objectives. Minimizing costs led to maximizing capacity utilization, while environmental objectives result in reduced production levels to meet coverage requirements, and maximizing the minimum ratio expands more facilities. Using an epsilon constraint, the trade-off shows that the environmental budget limits the flexibility between the other total cost achievable and the minimum ratio. Comparing the baseline model and the disruption model underscores the importance of proactive disruption management in maintaining service levels and managing costs effectively. Ultimately, our study offers practical insights for optimizing pharmaceutical supply chains, balancing economic efficiency with social responsibility to navigate disruptions and challenges successfully.

The pharmaceutical industry's shift towards biological therapeutics has led to a transition from conventional batch production to continuous manufacturing. This change highlights the crucial need for effective process monitoring and control strategies to ensure consistent product quality and stability. Open-source benchmark simulation models have become essential tools for refining these processes, offering a platform for testing research hypotheses. This study uses the production of Lovastatin as a case study for continuous biopharmaceutical production. A comprehensive dynamic model covering upstream and downstream components provides an integrated perspective of the production process. The study introduces a basic control system emphasizing realistic sensor and actuator integration to enhance simulation accuracy. It assesses the benchmark through open-loop and closed-loop simulations, offering an in-depth analysis of the KTB1 model's dynamic response and functionality. KTB1 represents a model-driven decision support tool that enables the evaluation of monitoring strategies, process design, process optimization, and control for biomanufacturing.

Low-Temperature Proton Exchange Membrane Fuel Cells (LT-PEMFC) are favored as an alternative power source due to their high efficiency, rapid initialization, shut-down cycles, and zero emissions. Developing an effective model for LT-PEMFC is essential. In this study, machine learning models are created for LT-PEMFC, utilizing techniques such as Gradient Boosting Regression (GBR), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) to predict cell voltage based on operating parameters. The dataset is generated using an in-house physics-based MATLAB model, complemented by experimental data from elsewhere. GBR exhibits superiority over XGBoost, LightGBM, and RF. These data-based models for LT-PEMFC, developed on generated datasets, achieve R${}^2$ $\ge$ 0.99 and MAPE $\le$ 0.06 during testing. These models are further validated on experimental data with R${}^2$ $\ge$ 0.90 and MAPE $\le$ 0.1. This underscores the ability to construct accurate data-based models and thus reducing reliance on extensive experimentation.

The present work aims to develop a novel chemical process for clean hydrogen and power production and simulate it accordingly through a unique thermodynamic equilibrium model. This particular process is based on a partial oxidation of hydrogen sulfide (H₂S) at superadiabatic conditions to study its respective chemical products. The simulation of superadiabatic partial oxidation of H₂S is developed through the present model for the first time in the Aspen Plus. The process is further studied by varying different operating variables with an overall goal of optimizing the H₂S conversion into hydrogen. The developed model predicts a satisfactory H₂ production flow rate coupled with a low-sulfur dioxide (SO₂) output within the superadiabatic partial oxidation regime at an operating pressure below 0.5 bar. The H₂S conversion into H₂ is then found to be 23.48 % at 0.25 bar. The overall energy and exergy efficiencies of the system are found to be 87.51 % and 70.08 % respectively. The dissociation of H₂S in the presence of stoichiometric air results in elemental sulfur and hydrogen production rates of 0.0019 kg/s and 0.0012 kg/s, respectively.

Minimizing power consumption in large-scale aerobic fermentation is essential for cost-effective operations. A mechanistic model of aerobic precision fermentation was developed integrating microbial growth parameters, thermodynamic data, and bioreactor properties. Results showed that agitation power dominated energy consumption at low oxygen transfer rates ($OTR$), shifting to aeration power (70 % of total) at high cell growth rates. In high $OTRs$, mixing time reduced to 60 s from an initial value of 211 s. Scale-up from 5 m³ to 100 m³ decreased total specific power by 88 %. Operating at elevated headspace pressure lowered agitation speed, reducing total power consumption at high $OTR$. Impeller to bioreactor diameter ratio impacted the required agitation speed without significantly altering total power demand. Experimental data in a 100 L case study indicated a 0.43 kW.m⁻³ average power requirement across a 96-hour fermentation period. Our model demonstrates effective strategies for minimization of power consumption in industrial-scale aerobic fermentations.

The extended and complex nature of agri-food supply chain systems results in food loss and an asymmetrical flow of information. It is vital to integrate the cultivation, harvest, processing, and distribution decisions for designing a sustainable agri-food supply chain to minimize overall cost and create employment opportunities alongside considering global concerns. A multi-objective, integrated, sustainable mathematical model is presented in this study to maximize the revenue and employment generated while reducing the environmental impacts. The criteria for the evaluation of farmlands are derived from literature, and Geographical Information System (GIS) is used to obtain geospatial data to assess the performance of diverse farmlands across various criteria. The farmlands are then assessed and prioritized using the Best Worst Method (BWM). Among all criteria for selecting the farmlands, favorable temperature and land-use have the highest and lowest impact, respectively. Furthermore, a pricing model is proposed to estimate the price in various customer zones. The robust possibilistic model is suggested to take into account weather patterns, transportation costs, and customer zone demand under uncertain situation. The proposed model is illustrated in the Stevia processing plant in Iran and the tradeoffs between different model parameters and objective functions are studied, and the validity of the model is assessed by sensitive analyses. The outcomes show that to meet robustness, the number of active farmlands and warehouses should be increased by about 11%, which imposes a 10% cost on the model. Based on sensitive analysis, increases in production capacity and demand result in a significant rise in the profit function (12% and 16%, respectively), despite the fact that improvements to farmland and warehouse capacity have little effect on profit, indicating the need for managers to prioritize production rate and advertising. Moreover, the results show the best location for planting stevia, the optimum production rate, the proper number of warehouses, and their capacities in each period.