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Purpose A life cycle assessment was performed for the production of a total mixed ration (TMR) fed to finishing feedlot cattle in California, USA. The goal was to determine the climate change impact of the feed supply chain associated with the production of 1 kg finishing TMR (kg CO2e/kg TMR). A secondary goal was to compare the climate change impact of feed versus finished beef (kg CO2e/kg live weight). Methods The TMR was based on feeds commonly fed to finishing cattle in California. The Livestock Environmental Assessment and Performance Partnership (LEAP) guidelines were followed for inventory data collection. System boundaries included the production of crops and feed additives, transportation of TMR components, and compound feed production. Data were sourced from national databases and Ecoinvent™ unit process data. Three scenarios were assessed as a result of allocation at the transportation step: Scenario A (100% empty return load); Scenario B (50% empty return load): and Scenario C (0% empty return load). Energy, mass, and economic allocation, and system expansion of dried distillers grain solubles (DDGS) were assessed for sensitivity analysis. Total feedlot emission data from Stackhouse-Lawson et al. (2012) were used to compare to impacts of TMR production. Results Total emissions were determined to be 0.630 kg CO2e/kg TMR for Scenario A, 0.576 kg CO2e/kg TMR for Scenario B, and 0.521 kg CO2e/kg TMR for Scenario C. Corn production, transportation, and liquid premix production were primary contributors to the life cycle impacts of TMR production. Mass-based allocation of DDGS was found to have the most significant effect on overall impacts of the finishing TMR, with a 42% increase in life cycle emissions compared to other allocation methods. For Scenario A, feed used in Angus feedlot production contributed to 76% of total Angus feedlot emissions. Additionally, feed used in Holstein feedlot production contributed to 58% of total Holstein feedlot emissions. Conclusions and recommendations The present study demonstrates a need to better assess the feed supply chain of feedlot beef production in order to accurately identify areas that have the most significant impacts on overall emissions. This may aid in minimizing impacts associated with feed production and, by extension, beef production. The present study may also serve to inform future decisions for improvements or alterations of the LEAP guidelines.

This study reviewed and unveiled the economic sizes and managing systems in aquaculture production. The focus had been on developing new technologies and management systems in aquaculture production that can produce fish food on an economically competitive basis while still maintaining environmental health. The technique of recirculating aquaculture system (RAS) had been discussed. Economic issues such as: adequately available quality water, economic sizes, management issues, financial capabilities, various investment options, species selection, cost of production, capitalization cost of unit process, cost of pumping and bio-filtration, gas stripping and pH control, solid wastes removal, economically competitive scale, labor requirements as wellas predicted cost of production. The economic comparison of broilers and catfish production were evaluated. Also, challenges against the effective aquaculture technological application and the prospects to economic realities and management systems in aquaculture production were also highlighted. It could be concluded that economic realities and management of RAS is the soft and live wire in aquacultural development especially in developing countries like Nigeria. It is therefore, recommended that fish farmers need to engage and be trained on the use of this new fish production technology for increase production and economy benefits.

Lacking unit process data is a major challenge for developing life cycle inventory (LCI) in life cycle assessment (LCA). Previously, we developed a similarity-based approach to estimate missing unit process data, which works only when less than 5% of the data are missing in a unit process. In this study, we developed a more flexible machine learning model to estimate missing unit process data as a complement to our previous method. In particular, we adopted a decision tree-based supervised learning approach to use an existing unit process dataset (ecoinvent 3.1) to characterize the relationship between the known information (predictors) and the missing one (response). The results show that our model can successfully classify the zero and nonzero flows with a very low misclassification rate (0.79% when 10% of the data are missing). For nonzero flows, the model can accurately estimate their values with an R2 over 0.7 when less than 20% of data are missing in one unit process. Our method can provide important data to complement primary LCI data for LCA studies and demonstrates the promising applications of machine learning techniques in LCA.

In many industrial applications, laser powder bed fusion (L-PBF) has been recognized for its flexibility in Net Shape Manufacturing. During the process, the feedstock is deposited and selectively fused with a thermal joining via laser power. In this work, the unit process life cycle inventory methodology (UPLCI) was used to discretize energy consumption and material losses for modeling the L-PBF process. A reusable approach in terms of materials, parameters, and calculation tools is presented, to estimate the energy consumption and mass loss in practical evaluations of production lines. Calculations of energy were obtained and classified as basic, idle, and active energy. Theoretical equations were also shown to relate the most important parameters of the process with its energy consumption. Finally, a case study is presented to analyze the UPLCI capacity to improve energy consumption in the manufacturing of medical devices.

We study communication systems over band-limited Additive White Gaussian Noise (AWGN) channels in which the transmitter’s output is constrained to be symmetric binary (bipolar). We improve the available Ozarow-Wyner-Ziv (OWZ) lower bound on capacity which is based on peak-power constrained pulse-amplitude modulation, by introducing new schemes (achievability) with two advantages over the studied OWZ schemes. Our schemes achieve a moderately improved information rate and they do so with much fewer sign transitions of the binary signal. The gap between the known upper bound, which is based on spectral constrains of bipolar signals, and our new achievable lower bound is reduced to 0.93 bits per Nyquist interval at high SNR.

Particle scale modelling of comminution processes can provide significant insight into the flow of particles, their breakage, the effect of slurry, wear and energy utilisation within these machines. The ability to use such models to assist in faster and lower cost design of new comminution devices and in the improvement of existing ones will be critical to the ability of industry to respond to the substantive challenges facing mineral processing in the next decade. These challenges are reviewed and drivers for change are discussed. Understanding individual unit process performance needs to be in the context of the flowsheets in which they are used so this is also reviewed. Advances in particle based comminution modelling are presented with this work divided into two parts. This first part focuses on recent advances in particle based modelling of crushing. Three crusher types are used to demonstrate these capabilities: 1. Twin roll crusher 2. Cone crusher 3. Vertical Shaft Impactor (VSI) These show the nature and level of fidelity that is now possible to include in particle scale crusher models including breakage of non-spherical particles and prediction of the product size distribution and throughput.

Plasma spraying is an energy intensive manufacturing process to provide function coatings. As such, energy consumption analysis always is promising for the development of green plasma spraying. In this study, a systematic energy consumption model of plasma spraying was presented considering various working states and processing parameters with unit process life cycle inventory method. Verification showed that energy consumption could be accurately calculated with this model within 5% errors. In light of this model, an L9 Taguchi test was carried out to investigate the relationship between energy consumption and coating quality (i.e., coating thickness, hardness, and porosity) with different processing factors (i.e., power, powder feeding rate, spraying jet velocity, and scanning step). It was indicated that power was the most influential factors for the energy consumption and the following factors were scanning step and spraying jet velocity. Besides, Energy analysis showed that the coating thickness and hardness are closely related to the unit energy consumption with a linear and quadratic function respectively. This work provides a basic guidance for quantifying energy consumption and could be extended for the sustainable development of plasma spraying.

Unit process life cycle inventory (UPLCI) is a modeling approach that enables researchers to estimate the energy use and material flow for a unit process. UPLCI models can be reused in characterizing a full manufacturing line, where a wide range of machines and materials are used. As part of a collaborative effort from various universities and industry researchers to create UPLCI models for all common manufacturing processes, this paper presents UPLCI for the metal injection molding (MIM) process, which is in the mass conserving category of the taxonomy of manufacturing processes. In addition to the energy required for injection molding of the metal-polymer feedstock and for the subsequent debinding and sintering processes, the energy consumption during idle and standby periods are also captured. The application of the UPLCI model for MIM process is demonstrated using an example case study. This UPLCI model provides opportunity for researchers to estimate the energy use for a sequence of manufacturing processes used to make a metal injection molded product.

Life cycle assessment can support decisions for improving the environmental performance of construction products. However, the amount of data required for developing life cycle inventories limits the adoption of LCA. This work associates the interpretation of the impact results of construction products at the unit process level with a quantitative definition for the foreground and background system, for guiding primary data collection towards foreground processes that can be affected by decision-makers in the construction sector. A set of construction products commonly used in Brazil is selected, and their cradle-to-gate life cycle inventories are modeled using the ecoinvent database (version 2). Life cycle impact assessment is performed using the ReCiPe Midpoint Hierarchist method. The contribution of each process during the life cycle of construction products for each impact category is quantified. These processes are associated with economic sectors, which are classified as belonging to the foreground or background system from the perspective of the construction industry. Foreground sectors are those controlled or influenced by the construction sector and are defined based on the production share consumed by the construction value chain. The elementary flows defining each impact category are also identified. Foreground processes show significant contributions to most impact results of construction products. Global warming, fine particulate matter formation, ozone formation, acidification, human carcinogenic toxicity, and terrestrial ecotoxicity are mainly caused by direct emissions and fossil fuel combustion in manufacturing processes. Land occupation for production activities contributes to land use change, while the consumption of fuels, raw materials, and water causes fossil and mineral resource scarcity and water consumption respectively. Freshwater and marine ecotoxicities and human non-carcinogenic toxicity have foreground contributions only for steel and copper products due to emissions from the landfilling of mining tails. Ionizing radiation and stratospheric ozone depletion are mostly driven by background processes. A reduced group of elementary flows covers a big share of the environmental impacts of most construction products. The results indicate priorities for life cycle inventory primary data collection of construction products, by focusing on foreground processes and the corresponding elementary flows that cause many of the potential embodied impacts of construction. Increasing the availability of primary data for these processes improves the reliability of LCA-based decisions in the construction sector, especially in countries which still lack local LCI databases.

Solar-chemical production is one of the most promising options for producing valuable chemicals from greenhouse gases. An economically attractive and industrially applicable solar-chemical production device not only requires catalyst and/or reactor design, but also auxiliary unit process design, process integration, and optimization. Herein, we report a state-of-the-art monolithic solar-chemical production device having 8.03% solar to CO conversion efficiency and 0.77 to 31.9% CO2 one path conversion. Since the monolithic device directly couples a photovoltaic cell and a CO2 electrolyzer, the power loss due to a current converter can be avoided. According to the solar-chemical production device, a comprehensive process design accounting for CO2 to CO conversion, unreacted CO2 separation, and recycling structure is provided. The process model shows good agreement with experimental data for CO2 conversion in the electrolyzer. A process level techno-economic evaluation and a comprehensive review are also presented to highlight the current state and the economic feasibility of the developed device. Thereafter, we provide a sensitivity analysis in terms of CO2 conversion, membrane cost, solar to chemical efficiency, and current density necessary for economically profitable CO production. The equivalent CO sales cost from a 4 MW production plant is estimated to be $10.9 per kg and the corresponding carbon tax compensating for the price gap of the current market price is $6.6 per kg CO2. The sensitivity analysis demonstrates that >80 mA cm−2 current density or 22% CO2 conversion is desirable to effectively compete with the conventional CO production process.

Microplastics are classified as emerging pollutants of the aquatic environment, necessitating a comprehensive understanding of their properties for successful management and treatment. Wastewater treatment plants (WWTPs) serve as point sources of microplastic pollution of the aquatic and terrestrial (eco)systems. The first part of this review explores the basic definitions of microplastics, sources, types, physical and chemical methods of identifying and characterizing microplastics in WWTPs. The next part of the review details the occurrence of microplastics in various unit processes of WWTPs and sewage sludge. Followed by this, various methods for removing microplastics from wastewater are presented. Finally, the research gaps in this area were identified, and suggestions for future perspectives were provided. HIGHLIGHTS Wastewater treatment plants act as point sources of microplastic pollution.; Microplastics in wastewater treatment plants are diverse in composition, size, shape and origin.; Microplastics are seen in all unit processes of wastewater treatment plants.; Wastewater treatment plants remove bulk of microplastics entering them.; Majority of microplastics enter the environment from sludge generated in wastewater treatment plants.

Environmental impact minimization is an important aspect for improving the sustainability performance of manufacturing processes. Quantifying the environmental impacts of unit manufacturing processes requires systematic modeling of process life cycle inventories. This paper develops an inventory model for infeed centerless grinding based on the unit process life cycle inventory methodology. The developed model estimates the energy consumption, material losses, and consumable fluids, for a typical high-volume production setup. Process consumption data is estimated from parameters directly used for process setup as well as machine and tool specifications that can be easily obtained from technical documentation and product geometrical specification. The developed model is demonstrated using a case study involving the infeed centerless grinding of an Inconel 718 shaft on a Cincinnati Twin-Grip Centerless Grinding Machine.

Made available in DSpace on 2022-04-28T19:47:12Z (GMT). No. of bitstreams: 0 Previous issue date: 2022-03-01 The life cycle inventory (LCI) data of a gear hobbing was obtained by means of the methodology unit process life cycle inventory (UPLCI), to conduct a comparative life cycle assessment (LCA) between hobbing assisted by flood lubrication (FL) and minimum quantity lubrication (MQL). The results pointed out 4 among 11 normalized environmental impact categories totalized more than 80% of the accumulated impacts: Fossil Depletion (43%), Climate Changes (19%), Terrestrial Acidification (11%), and Freshwater Consumption (8%). The identified hotspot in the case study was the input flow of raw material for the system “Hobbing Machine,” which was linked to more than 75% of the total amount of normalized potential environmental impacts. Once, changes on raw material depends on the gear design, the research focused on the environmental aspects of energy and cutting fluid consumption, which depends directly on the hobbing process parameters. The introduction of MQL provided reduction of 70.77% on the total amount of normalized potential impacts, while the strategies to reduce electric energy consumption by the machine tool accounted only for 3.74%. Nevertheless, when raw material flow is considered in the LCA, it turns into the process hotspot, due to high energy demanded in the steel-making process, forging, and turning operations to shape the semi-finished gear. The relevance of the key environmental aspects, electric energy, cutting fluids, and raw material, can vary significantly according to the gear size itself. The performed case study was considered a pilot project for the hosting company and can be scaled up to a whole gear manufacturing plant to identify manufacturing cells, which are eligible to optimization in the use of cutting fluids and electric energy by the machine tools. Department of Production Engineering Research Group On Sustainability Engineering Center for Science and Technology Management Federal University of São Carlos, SP Department of Production Engineering Research Group On Engineering and Product Life Cycle Management Federal University of São Carlos, SP Center for Advanced and Sustainable Technologies São Paulo State University, SP Center for Advanced and Sustainable Technologies São Paulo State University, SP

Purpose: In life-cycle assessment (LCA), environmental technologies are often modelled as “black-box processes”, where inputs and outputs are typically not linked through physical and/or (bio) chemical relationships. This limits transparency and usability of environmental modelling of resource systems for which the conversion of materials and chemical substances in the materials is essential for the environmental performance. We introduce an advanced “process-oriented” modelling framework allowing quantitative and parameterised physical-chemical relationships between input material composition, conversion process units and subsequent output products, promoting mass and substance balanced conversion modelling and environmental assessment. Methods: A dedicated LCA model, EASETECH, has been used to provide a user-friendly platform for performing advanced LCA of complex technologies, without the need for additional software/tools. In the modelling framework, the technology is subdivided into individual unit processes. In each process, the characterisation of the input feedstock material into biochemical, physical, chemical and nutritional properties is taken into consideration in each multi-output production flow. For each unit process, the processes governing the mass/energy/substance transition and transformation are described by mathematical equations (i.e. relationships between inputs and outputs) through the use of parameters. A range of new operators were developed to establish these relationships that allow for non-linear responses whereby changes in one flow can give a non-linear response in other flows. The modelling framework and the involved operators are explained and applied to a biorefinery case study. Results and discussion: The model facilitates “tracking” of the feedstock material properties from the input to the final products, by establishing mass, substance and energy balances for each conversion unit process. In addition, the process-oriented modelling framework appropriately represents material/substance transition and transformations. The choice of process parameters has considerable importance for the overall results. This was illustrated by one-at-a-time changes in parameter values in two different biorefinery unit processes (i.e. hydrolysis, and fermentation and distillation). In addition, the relevance of feedstock characteristics for the performance of the individual unit processes was proved with fixed parameter sets with different feedstocks. The biorefinery case study demonstrated that the LCA model can be applied to technology cases with different process configurations (e.g. different efficiencies) and different input feedstock properties, where it automatically adjusts to these changes in properties. Conclusions: The advanced process-oriented modelling framework offers more flexible modelling of the conversion technology than previously available, improved options for technology development in view of environmental performance, and potentially more accurate results. This provides a significantly improved basis for environmental modelling and decision-making in relation to resource systems.

Increased water scarcity is driving water utilities to consider alternative water supply options (WSOs). Identifying the most sustainable WSOs that meet environmental, economic, and social objectives is challenging due to the overwhelming number of both assessment criteria and treatment configurations, including a growing number of potable reuse options. Conventional approaches using process-based life cycle assessments are data-intensive and site-specific, and therefore not suitable for the early stages of assessing WSOs. To address this gap, we have combined hybrid multi-regional input-output-based life cycle assessment (MRIO-LCA), social impact analysis, and multi-criteria decision analysis (MCDA) to develop a novel, flexible framework with unit process level resolution for the comprehensive evaluation of WSOs. Here we describe the formulation of the framework along with an application to two US utilities considering alternative WSOs. In both cases, given sufficient levels of community acceptance and optimum system design, the results favoured potable reuse WSOs due to their climatic resilience, cost effectiveness and lower environmental impacts (compared to other non-conventional options such as desalination). The generalised and globally applicable framework developed for this study can be used to assess sustainability trade-offs between diverse WSO configurations, providing valuable insights for decision-making processes at the early planning stage, as a complement to existing urban water supply scenario and optimisation models.

Process-property relations are central to ICME. Engineers are often interested in using these relations to make decisions on process configurations to achieve desired properties. This is known as the inverse problem and is typically solved using forward models (physics-based or data-based) in an optimization loop, which can sometimes be expensive and error prone, especially when used on process chains with multiple unit steps. We propose a Bayesian networks-based approach for modeling process-property relations that can be used for inverse inference directly. The solutions thus found can serve as good starting points for a more detailed simulation-based search. We also discuss how unit process models can be composed to do inverse inference on the process chain as a whole. We demonstrate this in a wire-drawing process where a wire is drawn in multiple passes to achieve desired properties. We learn a Bayesian network for a unit pass and compose it multiple times to infer process parameters of all passes together.

In accordance with the decommissioning plan for the Kori Unit 1 NPP, the reactor coolant system will be chemically decontaminated as soon as possible after permanent shutdown. This study developed the chemical decontamination process though the development project of decontamination technology of reactor coolant system and dismantled equipment for NPP decommissioning, which has been carried out since 2014. In this study, Oxidation/reduction process was conducted using system decontamination process development equipment of lab scale and was divided into unit and continuous processes. The optimal process time was derived from the unit process, and decontamination agent and the number of process were derived through the continuous processes. Through the unit process, the oxidation process took 5 hours and the reduction process took 4 hours. As optimum decontamination agent, the oxidizing agent was 200 mg·L-1 Permanganic acid + 200 mg·L-1 Nitric acid and the reducing agent was 2000 mg·L-1 Oxalic acid. In the case of the number of processes, all oxide films were removed during the two-cycle chemical decontamination process of STS304 and SA508. In the case of Alloy600, all oxide films were removed when chemical decontamination was performed for three cycles or more.

This study updates the Petroleum Refinery Life Cycle Inventory Model (PRELIM) to provide a more complete gate-to-gate life cycle inventory and to allow for the calculation of a full suite of impact potentials commonly used in life cycle assessment (LCA) studies. Prior to this update, PRELIM provided results for energy use and greenhouse gas emissions from petroleum refineries with a level of detail suitable for most LCA studies in support of policy decisions. We updated the model to add criteria air pollutants, hazardous air pollutants, releases to water, releases to land, and managed wastes reflecting 2014 reported releases and waste management practices using data from the U.S. Environmental Protection Agency Greenhouse Gas Reporting Program, National Emissions Inventory, Discharge Monitoring Reports, and Toxic Release Inventory together with process unit capacities and fuel consumption data from the U.S. Energy Information Administration (U.S. EIA). The variability of refinery subprocess release factors is characterized using log-normal distributions with parameters set based on the distribution of release factors across facilities. The U.S. EPA Tool for the Reduction and Assessment of Chemical and Environmental Impacts life cycle impact assessment (LCIA) method is used together with the updated inventory data to provide impact potentials in the PRELIM dashboard interface. Release inventories at the subprocess level enable greater responsiveness to variable selection within PRELIM, such as refinery configuration, and allocation to specific refinery products. The updated version also provides a template to allow users to import PRELIM inventory results into the openLCA software tool as unit process data sets. Here we document and validate the model updates. Impact potentials from the national crude mix in 2014 are compared to impacts from the 2005 mix to demonstrate the impact of assay and configuration on the refining sector over time. The expanded version of PRELIM offers users a reliable, transparent, and streamlined tool for estimating the effect of changes in petroleum refineries on LCIA results in the context of policy analysis.

In India, the living standard is getting upgraded with the development of the economy. This leads to consequent increase in the generation of wastewater causing damage to the environment in various aspects. The Electrocoagulation (EC) process has attracted a great deal of attention in treating various wastewaters because of its versatility and environmental compatibility. The present study was conducted to investigate the applicability of the EC technique in continuous mode for the treatment of Municipal wastewater (MWW) by using solar power through batteries. The solar powered EC is found to be appropriate as sole unit process to treat municipal wastewater. The effects of operating parameters such as current density (8–64 A/m2) and detention time (4–24 min) on chemical oxygen demand (COD), turbidity and total dissolved solids (TDS) removal were studied. The optimum conditions were determined as 40 A/m2 and 20 min by monitoring zeta potential (ζ) of the effluent. At the optimum conditions, removal efficiencies for COD (92.01%), turbidity (93.97%), and TDS (49.78%) were observed.

Life cycle assessment (LCA) carries significant uncertainties and imprecision due to a number of factors, including the framework’s linearity assumptions and the wide use of aggregate unit processes in practice. In this work, we exploit the unit manufacturing process (UMP) information model (ASTM E3012-16) to enable parametric environmental analysis of manufacturing systems without disrupting the traditional LCA workflow. We present a formal mapping of an extension of the ASTM E3012 data model and the ecoSpold2 data model. We then demonstrate the utility of this mapping by (1) generating life cycle inventory (LCI) data from an example UMP model representing a vertical milling process and (2) linking the results with an existing LCI database. To show value, we use the Brightway2 framework to process the LCI data and complete a LCA. We conclude by comparing LCA results generated from the parametric milling UMP model against LCA results of a similar milling unit process model from a commercial database.

When a system crashes, fast and accurate log-based fault diagnosis can remarkably reduce the recovery time of the system and avoid further economic losses. Especially for the nuclear power industry, recovery time will lead not only to economic losses but also to international repercussions. Nevertheless, the massive quantity of obscure log information and the existence of hidden nodes pose major challenges to fault diagnosis and root cause determination. To overcome these obstacles, we propose the nowhere to hide (NTH) methodology, an efficient method to diagnose faults and locate root causes. We implement log-node and node-log mapping to avoid vital data loss in collecting fault logs and hidden nodes; furthermore, we utilize the logic of the nuclear power unit process system to reveal the crucial information in fault logs and hidden nodes and their causality to determine the root cause. We evaluate the methodology in a real nuclear industrial environment. The results show that system administrators can efficiently determine the root cause with the proposed methodology. Finally, we discuss the enhancements that are underway to improve the methodology.

Unit process, open-water (UPOW) treatment wetlands are a unique type of constructed wetlands that are designed to promote photo- and microbiologically mediated natural water treatment proc...

Traditional sustainability assessment conducts life cycle economic, environmental, and social impact assessment individually and utilizes multi-criteria or optimization methods for decision making. This method is usually challenged by systems thinkers who believe that system is an integrated whole with elements interconnected and the analysis requires continuous learning and understanding. In manufacturing systems, there is still a lack of understanding on linkages of economic, environmental and social activities and impacts, especially the interactions based on manufacturing system behaviors. This hinders manufacturer strategic and tactic decision making at unit process, shopfloor, and enterprise levels. This articles proposes a dynamic sustainability assessment method based on system dynamics modeling of behaviors and systemically connects economic, environmental, and social impact assessments in manufacturing system sustainability assessment. The method identifies sustainable manufacturing problems with characteristics. Creative method design from systems science is used to incorporate knowledge from system dynamics, risk assessment, sustainability assessment, and life cycle engineering to tackle complex problem, and design the method structure. A case study demonstrated that modeling based method is able to assist manufacturing practitioners and researchers understand the underlying economic, environmental, and social relations in manufacturing systems.