Project description:Photovoltaic devices based on perovskite materials have a great potential to become an exceptional source of energy while preserving the environment. However, to enter the global market, they require further development to achieve the necessary performance requirements. The environmental performance of a pre-industrial process of production of a large-area carbon stack perovskite module is analyzed in this work through life cycle assessment (LCA). From the pre-industrial process an ideal process is simulated to establish a benchmark for pre-industrial and laboratory-scale processes. Perovskite is shown to be the most harmful layer of the carbon stack module because of the energy consumed in the preparation and annealing of the precursor solution, and not because of its Pb content. This work stresses the necessity of decreasing energy consumption during module preparation as the most effective way to reduce environmental impacts of perovskite solar cells.
Project description:There have been many studies on the optimal tuning and control performance assessment (CPA) of the PID controller. In the optimal tuning, the trade-off between the setpoint tracking and the disturbance rejection performance is a challenge. Minimum output variance (MOV) is very widely used as a benchmark for CPA of PID, but it is difficult to be observed due to the non-convex optimization problem. In this paper, a new multiobjective function, considering both the OV in the CPA problem and integral of absolute error, is proposed to tune PID for this trade-off. The CPA-related non-convex problem and tuning-related multiobjective problem are solved by teaching-learning-based optimization, which guarantees a tighter lower bound for MOV due to the excellent capability of local optima avoidance and has higher computational efficiency due to the low complexity. The numerical examples of CPA problems show that the algorithm can generate better MOV than existing methods with less calculation time. The relationship between the weight of the multiobjective function and the performance, including setpoint tracking, stochastic and step disturbance rejection, is revealed by simulation results of the tuning method applied to two temperature control systems. The proper adjustment of the weight with a multistage strategy can achieve the trade-off to obtain excellent setpoint tracking performance in the initial stage and satisfying disturbance rejection performance in the steady stage.
Project description:Introduction: This study assesses the environmental impacts of mannosylerythritol lipids (MELs) production for process optimization using life cycle assessment (LCA). MELs are glycolipid-type microbial biosurfactants with many possible applications based on their surface-active properties. They are generally produced by fungi from the family of Ustilaginaceae via fermentation in aerated bioreactors. The aim of our work is to accompany the development of biotechnological products at an early stage to enable environmentally sustainable process optimization. Methods: This is done by identifying hotspots and potentials for improvement based on a reliable quantification of the environmental impacts. The production processes of MELs are evaluated in a cradle-to-gate approach using the Environmental Footprint (EF) 3.1 impact assessment method. The LCA model is based on upscaled experimental data for the fermentation and purification, assuming the production at a 10 m³ scale. In the case analyzed, MELs are produced from rapeseed oil and glucose, and purified by separation, solvent extraction, and chromatography. Results: The results of the LCA show that the provision of substrates is a major source of environmental impacts and accounts for 20% of the impacts on Climate Change and more than 70% in the categories Acidification and Eutrophication. Moreover, 33% of the impacts on Climate Change is caused by the energy requirements for aeration of the bioreactor, while purification accounts for 42% of the impacts respectively. For the purification, solvents are identified as the main contributors in most impact categories. Discussion: The results illustrate the potentials for process optimization to reduce the environmental impacts of substrate requirements, enhanced bioreactor aeration, and efficient solvent use in downstream processing. By a scenario analysis, considering both experimental adaptations and prospective variations of the process, the laboratory development can be supported with further findings and hence efficiently optimized towards environmental sustainability. Moreover, the presentation of kinetic LCA results over the fermentation duration shows a novel way of calculating and visualizing results that corresponds to the way of thinking of process engineers using established environmental indicators and a detailed system analysis. Altogether, this LCA study supports and demonstrates the potential for further improvements towards more environmentally friendly produced surfactants.
Project description:The bio-based production of aromatics is experiencing a renaissance with systems and synthetic biology approaches promising to deliver bio-catalysts that will reach yields, rates, and titers comparable to already existing bulk bio-processes for the production of amino acids for instance. However, aromatic building blocks derived from petrochemical routes have a huge economic advantage, they are cheap, and very cheap in fact. In this article, we are trying to shed light on an important aspect of biocatalyst development that is frequently overlooked when working on strain development: economic and environmental impact of the production process. We estimate the production cost and environmental impact of a microbial fermentation process depending on culture pH, carbon source and process scale. As a model molecule we use para-hydroxybenzoic acid (pHBA), but the results are readily transferrable to other shikimate derived aromatics with similar carbon yields and production rates.
Project description:In this report, the environmental aspects of producing proton conducting ceramics are investigated by means of the environmental Life Cycle Assessment (LCA) method. The proton conducting ceramics BaZr0.8Y0.2O3-δ (BZY), BaCe0.9Y0.1O2.95 (BCY10), and Sr(Ce0.9Zr0.1)0.95Yb0.05O3-δ (SCZY) were prepared by the sol-gel process. Their material requirements and environmental emissions were inventoried, and their energy requirements were determined, based on actual production data. This latter point makes the present LCA especially worthy of attention as a preliminary indication of future environmental impact. The analysis was performed according to the recommendations of ISO norms 14040 and obtained using the Gabi 6 software. The performance of the analyzed samples was also compared with each other. The LCA results for these proton conducting ceramics production processes indicated that the marine aquatic ecotoxicity potential (MAETP) made up the largest part, followed by fresh-water aquatic ecotoxicity potential (FAETP) and Human Toxicity Potential (HTP). The largest contribution was from energy consumption during annealing and calcinations steps.
Project description:Methanol production has gained considerable interest on the laboratory and industrial scale as it is a renewable fuel and an excellent hydrogen energy storehouse. The formation of synthesis gas (CO/H2) and the conversion of synthesis gas to methanol are the two basic catalytic processes used in methanol production. Machine learning (ML) approaches have recently emerged as powerful tools in reaction informatics. Inspired by these, we employ Gaussian process regression (GPR) to the model conversion of carbon monoxide (CO) and selectivity of the methanol product using data sets obtained from experimental investigations to capture uncertainty in prediction values. The results indicate that the proposed GPR model can accurately predict CO conversion and methanol selectivity as compared to other ML models. Further, the factors that influence the predictions are identified from the best GPR model employing "Shapley Additive exPlanations" (SHAP). After interpretation, the essential input features are found to be the inlet mole fraction of CO (Y(CO, in)) and the net inlet flow rate (Fin(nL/min)) for our best prediction GPR models, irrespective of our data sets. These interpretable models are employed for Bayesian optimization in a weighted multiobjective framework to obtain the optimal operating points, namely, maximization of both selectivity and conversion.
Project description:In this study, we performed multi-objective model-based optimization of a potato-frying process balancing between acrylamide production and a quality parameter (yellowness). Solution analysis revealed that, for most of the Pareto solutions, acrylamide levels exceeded the EFSA recommendation. Almost equivalent optimal solutions were found for moderate processing conditions (low temperatures and/or processing times) and the propagation of the uncertainty of the acrylamide production model parameters led to Pareto fronts with notable differences from the one obtained using the nominal parameters, especially in the ranges of high values of acrylamide production and yellowness. These results can help to identify processing conditions to achieve the desired acrylamide/yellowness balance and design more robust processes allowing for the enhancement of flexibility when equivalent optimal solutions can be retrieved.
Project description:This article presents data affiliated with life cycle inventories, environmental impact and operational sustainability used in, the influence of raw material availability and utility power consumption on the sustainability of the ammonia process [1]. Scenario specific operating conditions were used to simulate the ammonia process based on unique constraints occurring within the Trinidad and Tobago energy sector. The data was collected using AspenⓇ Plus simulations and validated against plant operating data. The data consists of an economic cost evaluation as well as environmental impact using the CML-IA Baseline midpoint approach. The data was derived from life cycle inventories aligned to input/output material and energy flows within the ammonia process as well as life cycle assessment databases utilizing Ecoinvent v3.4. The data can be applied to the wider ammonia supply chain, aiding in achieving greater sustainable development within ammonia-based process industries.
Project description:Sustainable and responsible production and consumption are at the heart of sustainable development, explicitly mentioned as one of the sustainable development goals (SDG12). Life cycle assessment, with its integrated holistic approach, is considered a reference method for the assessment of the environmental impact of production and consumption. This paper presents a study on the environmental impacts of final consumption in Europe in five areas of consumption: food, mobility, housing, household goods, and appliances. Based on the selection of a set of representative products to meet food, mobility, housing, and other consumers' needs, environmental impacts of products are assessed over their full life cycle: from raw material extraction to production, distribution, use, and end-of-life phase. Life cycle inventories of representative products are multiplied by consumption statistics to assess the impact of an average European citizen in 2010 and 2015. Impacts are assessed considering the sixteen impact categories of the Environmental Footprint method. Results reveal that food is the most relevant area of consumption driving environmental impacts. Use phase is the most important life cycle stage for many impact categories, especially for the areas of consumption housing, mobility, and appliances. For the areas of consumption food and household goods, the most important life cycle phase is related to upstream processes, which corresponds to agricultural activities for food and manufacturing of products components for household goods. Apart from the results, the paper includes a detailed discussion on further methodological improvements and research needs to make use of the Consumer Footprint as an indicator for monitoring SDG 12 and for supporting sustainable production and consumption policies.
Project description:Regionalization of land use (LU) impact in life cycle assessment (LCA) has gained relevance in recent years. Most regionalized models are statistical, using highly aggregated spatial units and LU classes (e.g. one unique LU class for cropland). Process-based modelling is a powerful characterization tool but so far has never been applied globally for all LU classes. Here, we propose a new set of spatially detailed characterization factors (CFs) for soil organic carbon (SOC) depletion. We used SOC dynamic curves and attainable SOC stocks from a process-based model for more than 17,000 world regions and 81 LU classes. Those classes include 63 agricultural (depending on 4 types of management/production), and 16 forest sub-classes, and 1 grassland and 1 urban class. We matched the CFs to LU elementary flows used by LCA databases at country-level. Results show that CFs are highly dependent on the LU sub-class and management practices. For example, transformation into cropland in general leads to the highest SOC depletion but SOC gains are possible with specific crops.