Identification and partial characterization of cAMP-phosphodiesterases in the ciliate Euplotes raikovi.

In the ciliate Euplotes raikovi, two specific isoforms of cAMP- dependent phosphodiesterases were identified, one in the soluble and the other in the particulate fraction of the cell. Their activity was shown to be stimulated by Mg2+, insensitive to Ca2+ and cGMP, and scarcely inhibited by theophylline and 3-isobutyl-1-methyl-xanthine. They appear to be related to some phosphodiesterases of class II of other unicellular organisms in their biochemical features, and their enzymatic activity is up-regulated by elevation of intracellular cAMP level similarly to PDE-4 isoforms of mammals

Cross-talk between the autocrine (mitogenic) pheromone loop of the ciliate Euplotes raikovi and the intracellular cyclic AMP concentration

Cell type-specific protein signals, called pheromones, are constitutively secreted by Euplotes raikovi and bound back in autocrine fashion, with a positive effect on the vegetative (mitotic) cell growth. In cells growing suspended with their secreted pheromone, it was found that any interruption of this autocrine signaling loop was immediately followed by an effective enhancement of the basal intracellular cyclic AMP (cAMP) level. To establish a cause-effect relationship between these pheromone-induced variations in the cytoplasmic cAMP level and cell growth, cells ready to pass from a resting stage to a new growth cycle were conditioned either to incorporate a cAMP analog resistant to phosphodiesterase degradation, or to utilize cAMP released (following cell irradiation) from incorporated “caged” cAMP. Cells responded at every induced increase in their basal cAMP level by markedly decreasing their commitment to start a new growth cycle. It was deduced that the autocrine signaling of E. raikovi pheromones involves cAMP as inhibitor of its mitogenic activity

Conventional vs. alternative biogas utilizations: An LCA-AHP based comparative study

This study compares the performance and the environmental impacts of three biogas utilization processes: combined heat and power cycle, dimethyl ether production, and methanol production. The processes are evaluated based on key performance indicators and life cycle assessments. Results show that molar methane conversion in the reforming unit is 93% for both dimethyl-ether (DME) and methanol processes. Methanol reactor reports a per-pass hydrogen conversion of 21%. DME production achieves an 89% conversion rate. The chemical pathways exhibit a 33% molar conversion of COx, contributing to the conversion of CO2 into advanced chemicals. The environmental footprint is evaluated through a life cycle assessment. Biogas cogeneration has the lowest global warming potential since DME and methanol production processes are influenced by steam production and electricity intake from the national grid, which relies on fossil fuels. An analytic hierarchy process is employed to assess the overall performance of the processes. DME production performs best with a score of 39%, followed by cogeneration with 31%, and methanol production with 30%. Different case studies are examined by modifying the weight criteria for each impact category. The findings provide that cogeneration and DME technologies perform better in all the iterations

Life Cycle Assessment (LCA) of Dimethyl Ether (DME) Production: Fossil Fuels vs. Biogas

This study conducts a comprehensive Life Cycle Assessment (LCA) to compare the environmental impacts of two alternative pathways for producing dimethyl ether (DME): one utilizing conventional fossil fuel-based processes and the other relying on biogas feedstocks. LCA is employed as a robust tool for evaluating the sustainability of these fuel production methods. The authors meticulously quantify resource inputs and outputs for both production routes in the inventory analysis phase. For conventional fossil fuel-based production, the value of the impact categories is retrieved from SimaPRO database. On the other hand, for biogas-based production, Aspen HYSYS is utilised to perform process simulations. The results of these are used as input for LCA analysis. Our findings reveal significant disparities between the two production pathways. Biogas-derived DME exhibit lower greenhouse gas emissions and reduced dependence on finite fossil resources. The biogas route also provides valuable co-benefits, such as organic waste valorization and potential improvements in soil quality through feedstock cultivation. However, it is essential to recognize that biogas-based production requires more land and water resources than fossil fuel-based. Therefore, trade-offs between reduced carbon emissions and increased resource use should be carefully considered, particularly in regions with limited land and water availability

Using Reduced Kinetic Model for the Multi-Objective Optimization of Thermal Section of the Claus Process Leading to a More Cost-Effective and Environmentally Friendly Operation

The Claus process is a sulfur recovery unit wherein hydrogen sulfide is converted into the elemental sulfur. This study aims to model the thermal section of the Claus process, which consists of a reaction furnace and a waste heat boiler, as a configuration of two reactors, and subsequently optimize the entire section. Two different reduced kinetic schemes were provided for both units. Using the validated kinetics, mathematical models were developed. The waste heat boiler was modeled as a plug flow reactor with heat transfer, instead of a heat exchanger. The main objective was to maximize the amount of elemental sulfur at the end of the thermal section. Additionally, maximizing the amount of steam generated in the WHB was considered as a secondary objective, and the multi-objective optimization problem was solved. The sulfur production was improved 14.1% and 30% as a result of single- and multi-objective optimization studies. In addition, as an alternative, the Taguchi method was also used for optimization studies, and optimum values were determined. Using the Taguchi method, we determined that an increase in sulfur production by 24% is possible

Enhancing Computational Performances in Chemical Processes Costs and Emissions Prediction: a Surrogate Modelling Based Approach

The increasing amount of variables to be accounted for in chemical processes optimization and the need to have a systemic approach to include all the steps of the industrial production chain implies the exponential growth of the model equations to be solved at the same time. In fact, in order to have an optimal industrial system, the analysis should start from raw materials supply and include demand-side, process side and logistic from the meso- to the macro-scale perspective. Moreover, beside economics, environmental impact, flexibility and scheduling should be coupled in a multi-objective optimization loop. This approach results in a computational effort that is way higher than that required in the past for conventional process optimal design. Therefore, innovative computational strategies should be implemented in order to ease the optimization loop. During the last decade, surrogate modelling has seen renewed interest for this purpose in chemical process engineering and it has been widely used for feasibility analysis, optimization and optimal scheduling. In this preliminary study we exploit a surrogate modelling approach for costs and emissions calculation for a simple separation process. A distillation unit is simulated by means of ProSimPlus process simulator to retrieve a set of physical and economic data over the operating domain of interest. After that, the sampling strategy is selected according to the suggested standards and adopted to generate a surrogate modelling with a Response Surface Methodology approach by means of ALAMO software. The output variable of interest for this study have been identified as the unit costs and the emissions related to the energy consumption. Despite the complexity of chemical equilibrium in multistage units, the obtained results show good agreement with those generated by the phenomenological models with a computational time whose magnitude is two orders lower. In conclusion, this methodology is worth deeper studies in order to be exploited for more complex systems and have even more benefits with the increasing complexity of the case study when coupling more units in different configurations

Development of a surrogate model of an amine scrubbing digital twin using machine learning methods

Advancements in the process industry require building more complex simulations and performing computationally intensive operations like optimization. To overcome the numerical limit of conventional process simulations a surrogate model is a viable strategy. In this work, a surrogate model of an industrial amine scrubbing digital twin has been developed. The surrogate model has been built based on the process simulation created in Aspen HYSYS and validated as a digital twin against real process data collected during a steady-state operation. The surrogate relies on an accurate Design of Experiments procedure. In this case, the Latin-Hypercube method has been chosen and several nested domains have been defined in ranges around the nominal steady state operative condition. Several machine learning models have been trained using cross-validation, and the most accurate has been selected to predict each target. The resulting surrogate model showed a satisfactory performance, given the data available

Flexibility analysis of a distillation column: Indexes comparison and economic assessment

The worldwide shared definition of “optimal design” refers to the cheapest and simplest design able to perform the required job; most of the time this definition is strictly related to given operating conditions, i.e. the input variables are seldom subjected to considerable variations. However, in process engineering, plenty of cases don’t fit this definition due to the uncertain nature of the feedstock needed to be processed. Therefore, if a system is likely to undergo several and substantial perturbations, an a priori flexibility assessment can be crucial for the good performance of the operation. In chemical engineering the leading separation process is distillation. Hence the first purpose of this paper is to define a procedure and compare the different flexibility indexes found in literature in order to perform a simple distillation column flexibility assessment. The second goal of this paper is to couple the flexibility and economic aspects related to the distillation column investment costs and again to compare the different indexes economic behaviours