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Technological innovation has increasingly become important in addressing the most pressing problems in society. To create better and sustainable solutions, project teams often strive to bring the people they are designed for to the innovation process. While the human-centered design approach has become popular in guiding design in the private sector, its place in addressing larger challenges beyond human needs is still not well defined. Using our experiences in designing the WasteWATER app, an open-source decision making tool intended to help design sustainable water reclamation systems, we illustrate the synergy between analytical research and human-centered design, through the convergence of design and anthropology in creating applied outcomes in universities. Analytical research can help understand complex problems such as the sustainability of water reclamation. At the same time, embedding design thinking in applied research projects can help researchers in academic institutions refine their solutions through collaborative and iterative design with stakeholders.

The availability of sufficient water supply is a challenge many municipalities have faced in recent decades and a challenge that is expected to intensify with time. While several choices remain for selecting alternatives to freshwater sources, water reclamation offers an opportunity for sustainable resource recovery. Nonetheless, tradeoffs exist in the selection of the most sustainable technology for recovering resources from wastewater when long-term impacts are taken into consideration. This article investigates the factors influencing the environmental and economic impacts of resource recovery technologies through the analysis of life cycle environmental and economic impact case studies. Key characteristics were extracted from life cycle assessment and life cycle cost case studies to evaluate the factors influencing the sustainability of the resource recovery systems. The specific design parameters include the type of resources to be recovered, technology utilized, scale of implementation, location, and end users. The design of sustainable resource recovery systems was found to be largely driven by scale, location (e.g., as it pertains to the energy mix and water quality restrictions), and the scope of the system considered. From this analysis, a decision framework for resource recovery-oriented wastewater management was developed and then applied to an existing case study to demonstrate its usability.

To understand the past successes and future opportunities for applying Industry 4.0 technologies toward manufacturing sustainability assessment, this state-of-the-art review examines previous literature at the intersection of these domains. Specifically, we focus on the application of Industry 4.0 technologies in the context of the following stages in manufacturing sustainability assessment: (i) planning, infrastructure development, and problem definition, (ii) performance measurement, (iii) results interpretation and decision-making, as well as (iv) intervention, control, and validation strategies. On the basis of the literature review, we present the trends, benefits, research gaps, and required future work for holistically integrating the research domains mentioned above. To accompany this literature review, we performed a meta-analysis of 14,498 articles and compared them to 316 articles compiled from a focused literature search. Based on database-controlled keywords, networks representing keyword co-occurrences were constructed to reveal clusters of related terms and evaluate overall term centrality (i.e., importance). Results from this analysis showed that the two datasets exhibited a similar network structure and also helped reveal Industry 4.0-related opportunities for manufacturing sustainability assessment. They include areas such as automation, robotics, and advanced inspection technologies, which are yet to be exploited in manufacturing sustainability assessment. Further research is needed to investigate whether the incorporation of such areas can (i) facilitate more robust and accessible assessments of manufacturing sustainability and (ii) make manufacturing systems themselves more sustainable.

Manufacturers often use reactive and preventive maintenance practices on their machinery, which can result in substantial economic losses that could be prevented with advanced maintenance strategies. While past research has defined frameworks and system architectures for smart manufacturing, guidance for implementing prognostics and health management (PHM) solutions for manufacturing is needed. This article presents “health-ready” concepts and characterizes health-ready capabilities for smart manufacturing systems to support the implementation of PHM solutions. Future work is recommended to evaluate the health-readiness of manufacturing assets and validate the expected value of increasing its health-readiness capability level.

This paper proposes a multi-criteria decision-making analysis of the alternatives for smart and sustainable machining processes to provide visibility and clarity on the factors that can affect production performance. Identification of such parameters can aid in the adoption of smart manufacturing technologies. The framework developed for decision making utilizes fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compare alternative machining scenarios. Machining with Tool Condition Monitoring (TCM) and machining with Computational Fluid Dynamics (CFD) for modeling ambient conditions are analyzed for their application and form use cases in the framework. Feasibility of TCM via vibration analysis when milling 17-4 Stainless Steel is investigated and a positive trend is observed between the surface roughness of the work piece and the cutting tool vibration at time steps where tool wear is predicted. Thus, a viable low-cost solution for TCM is available. The ambient conditions of the machining environment have been modelled with CFD to study temperature and airflow gradients. The CFD model can be used to reduce thermal errors for precision machining and enhance operator efficiency. The result from the decision-making framework shows a clear preference for smart machining alternatives as compared to the conventional machining. In all, machining with TCM and CFD is found to be the most preferred.

Over the past few decades, wastewater has been evolving from a waste to a valuable resource. Wastewater can not only dampen the effects of water shortages by means of water reclamation, but it also provides the medium for energy and nutrient recovery to further offset the extraction of precious resources. Since identifying viable resource recovery technologies can be challenging, this article offers a review of technologies for water, energy, and nutrient recovery from domestic and municipal wastewater through the lens of the scale of implementation. The system scales were classified as follows: small scale (design flows of 17 m3/day or less), medium scale (8 to 20,000 m3/day), and large scale (3800 m3/day or more). The widespread implementation of non-potable reuse (NPR) projects across all scales highlighted the ease of implementation associated with lower water quality requirements and treatment schemes that resembled conventional wastewater treatment. Although energy recovery was mostly achieved in large-scale plants from biosolids management or hydraulic head loss, the highest potential for concentrated nutrient recovery occurred in small-scale systems using urine source separating technologies. Small-scale systems offered benefits such as the ability for onsite resource recovery and reuse that lowered distribution and transportation costs and energy consumption, while larger scales benefited from lower per unit costs and energy consumption for treatment. The removal of pharmaceuticals and personal care products remained a challenge across scales, but unit processes such as reverse osmosis, nanofiltration, activated carbon, and advanced oxidation processes exhibited high removal efficiency for select contaminants.

The COVID-19 pandemic has imposed new challenges to maintaining sustainability in our manufacturing operations. With such high variability in demand for urgently needed products (e.g., personal protective equipment, testing technologies) and shifts in the needed capabilities of already complex production systems, sustainability challenges concerning waste management, life cycle impact characterization, and production operations have come to light. An extensive amount of data can be extracted from manufacturing systems, but it is not yet being used to improve the performance of production systems and maintain sustainability strategies during times of distress. This article proposes the concept of a Digital Depot. Being virtual in nature, the depot can contain plans and data for many different types of crises and contain a wider array of products than would be available in a physical, national stockpile. The information could be made available on demand to a national base of manufacturers to help them swiftly pivot to the production of critically needed goods while building on their existing manufacturing capabilities. The contents of the Digital Depot would be applicable to several stages pertinent to manufacturing operations including product definition, production planning information, asset and factory-level data, as well as data concerning the supply chain, distribution, and end-of-life stages. Future work is recommended in the development of templates for robust and secure data sharing, as well as multi-disciplinary identification of businesses cases for data-driven collaborative and sustainable manufacturing practices enabled by the Digital Depot.

Unscheduled downtime in manufacturing systems can be a major source of lost productivity, profits, and, ultimately, reduced process quality and reliability. However, the incorporation of asset condition management (ACM) into manufacturing systems offers an approach to improve equipment and plant operations by providing real-time condition awareness, system diagnostics, and estimates of future health to enable predictive maintenance. ACM is a framework for assessing the current and future state of health of a manufacturing system and integrating that knowledge with enterprise applications to meet the demand of production operations. In manufacturing systems, successful operations rely on the ability to maintain production assets at their optimal working levels to optimize operations and system performance. Some large corporations have made great strides in incorporating smart technologies to enhance their asset management strategy; however, small- and medium-sized enterprises (SMEs) face distinct challenges. One of the key challenges is that most SMEs do not have the wherewithal to invest in new machines nor is there standard guidance on how older machines can be integrated into an ACM solution, so that their end-to-end manufacturing process can be optimized from a health management point of view. This research presents a framework for ACM to facilitate its introduction into manufacturing systems based on their “health-ready” capabilities. Specifically, an ACM system architecture is defined for manufacturing systems, the health-ready principles and capability levels from the aerospace and automotive industries are adapted to the manufacturing domain, and the results from the outreach effort to the manufacturing community are discussed.

Water shortage and water contamination necessitate adopting a reverse logistics and a closed-loop supply chain approach, which is the process of moving wastewater from its typical final destination back to the water supply chain with different levels of treatment for reuse. Hence, the incorporation of sustainability concepts through life cycle assessments for selecting reclaimed water applications considering reverse logistics and closed-loop systems is receiving more attention. However, no prior studies have evaluated the trade-off between the reclaimed water quality and corresponding costs, environmental impacts and social benefits for different types of water reuse. The aim of this study is therefore to design possible scenarios for water reuse based on water reuse guidelines and evaluate the different types of end use based on the three dimensions of sustainability (i.e., economic, environmental and social aspects) simultaneously. The different reuse types considered include unrestricted urban reuse, agricultural reuse, indirect potable reuse (IPR), direct potable reuse (DPR), distributed unrestricted urban reuse, as well as some degree of decentralization of treatment plants for distributed unrestricted urban reuse. The trade-off investigation and decision-making framework are demonstrated in a case study and a regret-based model is adopted as the support tool for multi-criteria decision-making. This study revealed that although increasing the degree of treatment for water reuse increases the implementation and operation and maintenance (O&M) costs of the design, it increases the value of resource recovery significantly, such that it can offset the capital and O&M costs associated with the treatment and distribution for DPR. Improving the reclaimed water quality also reduces the environmental footprint (eutrophication) to almost 50% for DPR compared to the other reuse scenarios. This study revealed that the distance between the water reclamation facility and the end use plays a significant role in economic and environmental (carbon footprint) indicators.

The unique and unprecedented challenges of the coronavirus disease 2019 (COVID-19) pandemic have resulted in significant disruptions to diverse manufacturing supply chains across the globe The negative economic impacts of these unexpected and rapid changes in demand and available supplies have been severe, and the economic sustainability of many businesses has been revealed as being highly sensitive to such changes COVID-19 will inevitably change manufacturing, potentially in a way that is not sustainable unless we factor sustainability into our “redesign ” Otherwise, the industry will remain overwhelmed in a reactionary cycle when the next major problem emerges, such as a lack of resources during a natural or man-made disaster In this article, we present strategies for addressing three sustainability challenges relevant to manufacturing introduced by the COVID-19 pandemic: (1) an increase in waste generation, (2) uncertainty in life cycle impacts, and (3) navigating new modes of operation for manufacturing To mitigate the sustainability challenges of COVID-19 and create a more resilient industrial sector, we need to assess the potential of each risk to product development and production processes We envision a systematic integration of sustainable manufacturing principles and metrics into the business practices of manufacturing enterprises, including the products they produce and the processes used to create them Realizing this vision will require greater availability and transparency of key data related to environmental and social sustainability factors, which can be used to create a clean and sustainable future in which pandemic and disaster readiness is realized through sustainable manufacturing © 2020 ASTM International All rights reserved

Life cycle assessments (LCAs) follow the ISO 14040 standard and consist of the following steps: 1) goal and scope definition, 2) life cycle inventory analysis, 3) life cycle impact assessment, and 4) interpretation. Prior literature reviews of wastewater treatment and water reuse LCAs have evaluated the methods implemented within these assessments. In lieu of manually tabulating the characteristic features of LCAs, Data Mining LCAs provides a method to facilitate the extraction of key characteristics. The process consists of the following:•Each journal article is converted to a text file and read in Python.•Search terms are defined for each characteristic of the LCA to be extracted.•By employing Python's regular expressions operations and the natural language toolkit (NLTK), the functional unit, life cycle impact characterization method, and the location of each case study are identified., Graphical_Abstract Image, graphical abstract

Energy crisis caused by the population growth, increasing demand, and the limited availability of fossil fuels has led to wastewater-based heat recovery emerging as an alternative energy source. As wastewater-based resource recovery systems are location-specific, a holistic view of their sustainability requires a comprehensive analysis of the effect of local conditions on these systems. This study evaluated the role of climate and population density on the economic and environmental sustainability of drain water heat recovery systems (DWHRS) and compared it to that of a medium-scale district heating system. The DWHRS installed in a cold location showed about 2–2.6 times higher energy recovery and 4 times higher greenhouse gas emissions than that in a hot place. The economic impact was predominantly influenced by the cost of electricity, while the environmental sustainability was found to decrease with a decrease in the ambient temperature and solar radiation. Lower population density increases economic sustainability and lowers environmental sustainability of the system. The DWHRS is more sustainable than the medium-scale district heating system in low population density regions irrespective of the climatic conditions. However, in cold places with a high population density, the trade-off between the economic and environmental sustainability must be evaluated to decide whether to install a small- or medium-scale system. Therefore, it is essential to perform a detailed location-specific analysis before deciding the optimal type of heat recovery system for the location under consideration. The model can be utilized as an evaluation tool to quantify the sustainability of the DWHRS and help consumers decide the most suitable method of waste heat recovery.

Wastewater is rich with valuable resources available for recovery. Although the resource recovery process can enable the offset of freshwater withdrawals, fertilizer production, and the use of fossil fuels, it comes at a cost. This review focuses on the environmental and economic impacts of recovering water, energy, and nutrients from wastewater. Specifically, life cycle assessments and life cycle cost analyses were reviewed to identify trends in wastewater-based resource recovery systems. Process conditions were found to have a significant influence on impacts in some instances. For example, the operating temperature for incineration could lower or eliminate N2O emissions altogether, while the flow rate and cold water temperature resulted in heat transfer efficiencies for thermal energy recovery systems ranging from ∼12% to 92%. Economies of scale were largely present in the processing of wastewater and biosolids, however, the re-distribution of reclaimed water exhibited diseconomies of scale as water networks increased in service size. This led to some studies exhibiting lower impacts for centralized systems, while others favored decentralization. Nonetheless, when averaged and categorized by scale, the greenhouse gas emissions for large-scale water reclamation systems (280 kg CO2-eq/person-year) was significantly lower than medium- and small-scale systems (4600 and 23740 kg CO2-eq/person-year, respectively). Future research is recommended in analyzing understudied technologies (e.g., environmental assessments of thermal energy recovery systems and wastewater-based hydropower technologies), investigating spatial-temporal contexts, and optimizing wastewater systems with multiple forms of resource recovery technologies across varied scales.

Society has confronted many challenges in recent decades, ranging from devastating wildfires and hurricanes to, most recently, the coronavirus disease 2019 (COVID-19) pandemic Our manufacturing sector plays a critical role in confronting these challenges and in the recovery process, as it lies at the helm of the comfort, well-being, and safety of our communities Manufacturers help bring to fruition innovative technologies that can maintain our way of life during times of stability (e g , smartphones and fuel-efficient vehicles) and that can enable a quick recovery during emergencies or natural disasters (e g , rapid testing technologies, ventilators, and photovoltaic panels) This article suggests ways to enhance the efficiency, sustainability, and resilience of the manufacturing sector in the post-COVID-19 era Considerations for the supply chain, manufacturing systems, and manufacturing processes at the nanoscale are discussed, © 2020 American Institute of Physics Inc All rights reserved

Manufacturing accounts for 37% of the global energy demand. Few researchers have conducted a holistic energy assessment of factories where energy beyond production equipment is accounted for, and environmental impact assessments of machine tool manufacturing are even more limited. All the while, factories need to understand where energy is used, how to allocate it to their products, and where savings lie. This paper quantifies the energy consumption and greenhouse gas emissions of a machine tool manufacturer located in Iga, Japan. Six plants within the factory are investigated whose primary operations range from precision machining to product assembly. Furthermore, a model of the energy consumption of each plant's HVAC system is developed and used to evaluate the environmental impacts and operating costs at alternative sites in China, Germany, and the US. Location was found to have a significant influence on the environmental impacts and resulted in changes in the annual energy consumption relative to the Iga baseline that ranged from −10% to 8%. Additionally, opportunities for improvement were identified by comparing the energy intensities of the equipment within the plants to industrial averages. Since the plants in this study use widespread manufacturing processes and varied climates were investigated, the methods and strategies that were developed can be extended to other discrete manufacturing facilities to identify opportunities for cleaner production.

The impairment of water bodies from nutrient pollution is a challenging environmental problem that could lead to high eutrophic conditions, fish kills, and human illness, while negatively impacting industries that rely on thriving water bodies. Onsite wastewater treatment systems (OWTSs) are a major source of nutrients, however no prior studies have conducted a holistic sustainability assessment of OWTSs that considers their ability to manage nutrients at the household-level in the United States. The aim of this study is therefore to evaluate the environmental and economic impacts of conventional and advanced OWTSs with respect to their ability to remove total nitrogen (TN). Septic tank and drainfield materials were varied for conventional systems, and the advanced systems evaluated consisted of aerobic treatment units (ATUs) and passive nitrogen reduction systems (PNRSs) with nitrification and denitrification stages. Life cycle assessment and life cycle cost analysis were performed to evaluate OWTSs operating in different soil and temperature conditions. Nutrient management of the advanced OWTSs outperformed the conventional systems (96.7-100% vs. 61-65% TN removal), and resulted in less than 40% of the freshwater (0.06-0.14 vs. 0.37-0.40 kg P-eq/kg TN) and marine eutrophication (0.04-0.06 vs. 0.54-0.65 kg N-eq/kg TN). However, the tradeoff for nutrient management was higher life cycle costs ($101-$121 vs. $45-$58 USD 2015/kg TN) and environmental impacts for the remaining impact categories. Lastly, when the TN removed by the drainfield was20%, the advanced system had lower impacts than conventional OWTSs across all impact categories except ecotoxicity.

Water reclamation and reuse not only reduce the negative impacts of wastewater to the environment, but also provide an alternative to withdrawal from natural water resources, forming a closed-loop water supply chain. The design of such a supply chain requires an appropriate sustainability assessment, which simultaneously accounts for economic, environmental, and social dimensions. In this study, a multi-objective optimization model was developed to minimize the costs and carbon footprint (as an environmental indicator), and maximize the value of resource recovery (representing a social benefit) of the water reclamation systems by locating the treatment facility, allocating the treatment capacity, selecting the treatment technology, and allocating customers (final reclaimed water users). The optimizer CPLEX 12.7 was applied and the Triangle Splitting Method was used for the optimization algorithm. The expansion of a water reclamation system in Hillsborough County, Florida was evaluated to illustrate the use of the model. The impacts of population density and elevation variation in the water service area on the model outputs were also investigated. Although the centralization of treatment facilities takes advantage of the economies of scale, the results revealed that simultaneous consideration of economic and environmental indicators favored decentralization of treatment facilities in the study area. This was mainly due to the significant decrease in water transfer requirements, especially in less populous areas. Moreover, the results revealed that contribution of population density to the optimal degree of decentralization of treatment facilities was significant.

CIRP Annals - Manufacturing Technology 62 (2013) 475–478 Contents lists available at SciVerse ScienceDirect CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp Assessment of lean and green strategies by simulation of manufacturing systems in discrete production environments Nancy Diaz-Elsayed a, *, Annabel Jondral b , Sebastian Greinacher b , David Dornfeld (1) a , Gisela Lanza (2) b a b Laboratory for Manufacturing and Sustainability, University of California, Berkeley, USA Institute for Production Science, Karlsruhe Institute of Technology, Karlsruhe, Germany A R T I C L E I N F O A B S T R A C T Keywords: Production planning Simulation Sustainable development Manufacturing is a resource-intensive and costly endeavor, yet the impacts of implementing a combination of lean and green practices in a manufacturing facility can hardly be forecasted and have typically been simulated, optimized, and valuated independently. This paper identifies an approach for incorporating both lean and green strategies into a manufacturing system; from data collection to the valuation of a system. Furthermore, a case study is presented of part production in the automotive sector, in which the implementation of a tailored combination of lean and green strategies resulted in the reduction of approximately 10.8% of the production costs of a representative part. s 2013 CIRP. 1. Motivation The cost of energy and resources are constantly increasing due to rising demand and limited supply. Furthermore, price trends can hardly be forecasted, so companies aim to successfully produce within large price ranges of energy and resources. One strategy to accommodate price fluctuations consists of passing markups to the customer. However, a price markup may require that improve- ments be made to the product. Alternatively, stable prices may be facilitated with increased production efficiency, which can be achieved by reducing resource consumption and improving the organization of the manufacturing system. This paper focuses on the latter strategy and presents an approach for the assessment of lean and green strategies by simulation of manufacturing systems. The optimal combination of strategies depends on the industry and relevant processes. For instance, the effects of reducing batch size and solvent consump- tion differs between a paint shop and an assembly line in the automotive industry. Therefore, solutions must be tailored to accommodate specific applications in order to maximize impact. scheduling [9–11] and additional process planning [12,13] techniques on factory level energy consumption. Herrmann et al. developed a simulation to analyze the effects of implementing lean and green manufacturing strategies on energy consumption and costs [14]. Extensive research has been conducted in the fields of ‘‘simulating and optimizing’’ or ‘‘simulating and valuating’’ the application of lean and/or green strategies in manufacturing systems. However, the literature lacks an assessment methodology capable of simulating, optimizing, and valuating a manufacturing system’s performance indicators while using a tailored combina- tion of lean and green strategies. Such an assessment methodology would allow companies to evaluate the effectiveness of strategies before spending funds to implement them on the shop floor. 3. Assessment of lean and green strategies by simulation The following section describes an assessment methodology for simulating, optimizing, and valuating a manufacturing system’s performance indicators while using a tailored combination of lean and green strategies. 2. Literature review 3.1. Modeling the current state This literature review focuses on simulation-based approaches, a special case of quantitative assessment. A variety of approaches have been constructed to simulate the effects of applying lean methods in manufacturing, i.e., illustrating differences between a real and ideal state [1], examining their impact on performance indicators [2–4], and bundling simulation results in Cost-time profiles [5,6]. Research in green manufacturing spans a variety of scopes within the factory, from the development of energy models of production equipment [7,8] to the assessment of machine * Corresponding author. 0007-8506/$ – see front matter s 2013 CIRP. http://dx.doi.org/10.1016/j.cirp.2013.03.066 In order to set up a simulation model of a manufacturing system and subsequently optimize it, a variety of data needs to be gathered. The required data for each product variant that is manufactured can be categorized into factory and operational data, energy and resource requirements, and employee involvement. All data needed to set up the discrete event simulation model of the current state can be measured on the shop floor or estimated with historical data. In order to create a simulation model capable of valuating the increase in a production system’s performance, installing multiple levels and building reusable modules is recommended. In a typical discrete production environment, one