Integrating Hybrid Life Cycle Assessment with Multiobjective Optimization: A Modeling Framework

Optimizing Wheat Yield, Water, and Nitrogen Use Efficiency With Water and Nitrogen Inputs in China: A Synthesis and Life Cycle Assessment

To meet the demand of the fast increasing population, enhancing the wheat (Triticum aestivum L.) yield and resource use efficiency by optimizing water and nitrogen (N) management can greatly improve agricultural sustainability and enhance regenerative farming in developing countries such as China. Based on 126 studies conducted in China between 1996 and 2018, using meta-analysis in combination with decision regression tree modeling and life cycle assessment (LCA), this study aimed to (1) quantify the effect of water and N input on wheat yield, water productivity (WP c ), and N use efficiency (NUE f ), and evaluate the subsequent environmental impact in different regions using LCA; and (2) evaluate, model, and rank the roles of environmental (e.g., soil nutrient status and climatic factors) and agronomic factors (e.g., water and N management practices) affecting wheat yield, WP c , and NUE f . The results showed that irrigation and N addition increased the average yield and WP c by 40 and 15%, respectively, relative to control treatments with no irrigation or fertilizer application. The mean water saving potential (WSP) and N saving potential (NSP) in China were estimated at 11 and 10%, respectively. Soil nutrient status [e.g., initial soil phosphorus (P) and potassium (K)] and soil organic carbon content affected the wheat yield, WP c , and NUE f more significantly than climatic factors [mean annual temperature (MAT)] or water and N management practices. The structural equation-based modeling indicated that initial soil nutrient condition impacted productivity and resource use efficiency more at the below optimal water and N levels than above. The risk-factor-based feature ranking indicated that site-specific environmental and soil condition was highly informative toward model construction but split input of N or water had less impact on yield and input use efficiency. LCA demonstrated that to further mitigate greenhouse gas emissions, water- or N-saving management should be promoted in China. Collectively, our research implies that long-term soil health and nutrient enhancement should be more beneficial for increasing yield and resource use efficiency in wheat production.

Applications of Multi-Objective Optimization to Industrial Processes: A Literature Review

Industrial processes provide several of the products and services required for society. However, each industry faces different challenges from different perspectives, all of which must be reconciled to obtain profitable, productive, controllable, safe and sustainable processes. In this context, multi-objective optimization has become a powerful tool to aid the decision-making mechanism in the synthesis, design, operation and control of such processes. The solution to the mathematical models provides the necessary tools to asses the system performance in terms of different metrics and evaluate the trade-offs between the objectives in conflict. The number of applications of multi- objective optimization in industrial processes is ample and each application has its own challenges. In the present literature review, a broad panorama of the applications in multi-objective optimization is presented, including future perspectives and open questions that still need to be addressed.

Evaluation of environmental consequences affecting human health in the current and optimal cropping patterns in the eastern Lorestan Province, Iran

Planning for optimal use of resources and reduction of environmental impacts, in addition to resource protection, is associated with increasing farmers' revenues and boosting the regional economy. Given the limited resources and environmental impacts of agricultural activities on human health, it is necessary to determine an appropriate cropping pattern. The present research aimed to maximize net profit and minimize environmental impacts, including the releases of carcinogens, noncarcinogens, ozone layer depletion, ionizing radiation, and respiratory inorganics and organics on human health. In this study, an optimal cropping pattern of irrigated and rainfed crops was proposed for the east of Lorestan Province using multi-objective nonlinear programming (MOP). Results showed that the cropping areas of chickpea, rapeseed, and potatoes decreased by 50% in the irrigated crop of MOP model and that of lentil in the MOP model of rainfed crops compared with the current pattern. Another important result was increases in the cropping areas of lentil and bean in the MOP pattern of irrigated crops and wheat in the rainfed MOP model. The environmental impacts of agricultural sector on human health can be reduced by determining an optimal cropping pattern. The implementation of this model in the region reduced the emissions of carcinogens (4%), noncarcinogens (9%), respiratory inorganics (17%), ionizing radiation (14%), ozone layer depletion (17%), and respiratory organics (15%) compared with the existing situation along with an increased net profit of $968,483. According to the findings, consideration of environmental objectives affecting human health is essential in the optimization of the cropping pattern. In addition to optimal use of water and land resources, using the proposed model helps to increase profits and reduce environmental consequences on human health.

Cost and Life-Cycle Greenhouse Gas Implications of Integrating Biogas Upgrading and Carbon Capture Technologies in Cellulosic Biorefineries

Gaseous streams in biorefineries have been undervalued and underutilized. In cellulosic biorefineries, coproduced biogas is assumed to be combusted alongside lignin to generate process heat and electricity. Biogas can instead be upgraded to compressed biomethane and used as a transportation fuel. Capturing CO₂-rich streams generated in biorefineries can also contribute to greenhouse gas (GHG) mitigation goals. We explore the economic and life-cycle GHG impacts of biogas upgrading and CO₂ capture and storage (CCS) at ionic liquid-based cellulosic ethanol biorefineries using biomass sorghum. Without policy incentives, biorefineries with biogas upgrading systems can achieve a comparable minimum ethanol selling price (MESP) and reduced GHG footprint ($1.38/liter gasoline equivalent (LGE) and 12.9 gCO_2e/MJ) relative to facilities that combust biogas onsite ($1.34/LGE and 24.3 gCO_2e/MJ). Incorporating renewable identification number (RIN) values advantages facilities that upgrade biogas relative to other options (MESP of $0.72/LGE). Incorporating CCS increases the MESP but dramatically decreases the GHG footprint (-21.3 gCO_2e/MJ for partial, -110.7 gCO_2e/MJ for full CCS). The addition of CCS also decreases the cost of carbon mitigation to as low as $52-$78/t CO₂, depending on the assumed fuel selling price, and is the lowest-cost option if both RIN and California's Low Carbon Fuel Standard credits are incorporated.

Life Cycle Assessment for the Design of Chemical Processes, Products, and Supply Chains

Design in the chemical industry increasingly aims not only at economic but also at environmental targets. Environmental targets are usually best quantified using the standardized, holistic method of life cycle assessment (LCA). The resulting life cycle perspective poses a major challenge to chemical engineering design because the design scope is expanded to include process, product, and supply chain. Here, we first provide a brief tutorial highlighting key elements of LCA. Methods to fill data gaps in LCA are discussed, as capturing the full life cycle is data intensive. On this basis, we review recent methods for integrating LCA into the design of chemical processes, products, and supply chains. Whereas adding LCA as a posteriori tool for decision support can be regarded as established, the integration of LCA into the design process is an active field of research. We present recent advances and derive future challenges for LCA-based design.

Toward Sustainable Chemical Engineering: The Role of Process Systems Engineering

Products from chemical engineering are essential for human well-being, but they also contribute to the degradation of ecosystem goods and services that are essential for sustaining all human activities. To contribute to sustainability, chemical engineering needs to address this paradox by developing chemical products and processes that meet the needs of present and future generations. Unintended harm of chemical engineering has usually appeared outside the discipline's traditional system boundary due to shifting of impacts across space, time, flows, or disciplines, and exceeding nature's capacity to supply goods and services. Being a subdiscipline of chemical engineering, process systems engineering (PSE) is best suited for ensuring that chemical engineering makes net positive contributions to sustainable development. This article reviews the role of PSE in the quest toward a sustainable chemical engineering. It focuses on advances in metrics, process design, product design, and process dynamics and control toward sustainability. Efforts toward contributing to this quest have already expanded the boundary of PSE to consider economic, environmental, and societal aspects of processes, products, and their life cycles. Future efforts need to account for the role of ecosystems in supporting industrial activities, and the effects of human behavior and markets on the environmental impacts of chemical products. Close interaction is needed between the reductionism of chemical engineering science and the holism of process systems engineering, along with a shift in the engineering paradigm from wanting to dominate nature to learning from it and respecting its limits.

Modeling and Simulation of Energy Systems: A Review

Energy is a key driver of the modern economy, therefore modeling and simulation of energy systems has received significant research attention. We review the major developments in this area and propose two ways to categorize the diverse contributions. The first categorization is according to the modeling approach, namely into computational, mathematical, and physical models. With this categorization, we highlight certain novel hybrid approaches that combine aspects of the different groups proposed. The second categorization is according to field namely Process Systems Engineering (PSE) and Energy Economics (EE). We use the following criteria to illustrate the differences: the nature of variables, theoretical underpinnings, level of technological aggregation, spatial and temporal scales, and model purposes. Traditionally, the Process Systems Engineering approach models the technological characteristics of the energy system endogenously. However, the energy system is situated in a broader economic context that includes several stakeholders both within the energy sector and in other economic sectors. Complex relationships and feedback effects exist between these stakeholders, which may have a significant impact on strategic, tactical, and operational decision-making. Leveraging the expertise built in the Energy Economics field on modeling these complexities may be valuable to process systems engineers. With this categorization, we present the interactions between the two fields, and make the case for combining the two approaches. We point out three application areas: (1) optimal design and operation of flexible processes using demand and price forecasts, (2) sustainability analysis and process design using hybrid methods, and (3) accounting for the feedback effects of breakthrough technologies. These three examples highlight the value of combining Process Systems Engineering and Energy Economics models to get a holistic picture of the energy system in a wider economic and policy context.

Optimization of multi-pathway production chains and multi-criteria decision-making through sustainability evaluation: a biojet fuel production case study

Selection of optimal technologies for novel biobased products and processes is a major challenge in process design, especially when are considered many alternatives available to transform materials into valuable products. Furthermore, such technological alternatives vary in their technical performances and cause different levels of economic and environmental impacts throughout their life cycles. Additionally, selection of optimal production pathways requires a shift from the traditional materials management practices to more sustainable practices. This contribution provides a method for optimizing multi-product network systems from a sustainability perspective by applying the GREENSCOPE framework as a sustainable objective function. A case study is presented in which the four GREENSCOPE target areas (i.e., efficiency, energy, economics, and environment) are evaluated by 21 preselected indicators as part of a multi-objective optimization problem of a biojet fuel production network. The biojet fuel production network evaluated in this study consists of four main elements: (1) feedstocks management, (2) conversion technologies, (3) co-products upgrading, and (4) auxiliary sections for in situ production of raw materials and utilities. For the sustainability objective function, the 21 indicators are analyzed considering multiple perspectives of stakeholders to study their influence on the decision-making process. It is, different sets of weighting factors are assigned to each of the four target areas. Hence, this sustainability evaluation from different stakeholders' perspectives allows identifying optimal networks, specific target areas with great potential for improvements, and processing steps with great influence in the entire network performance. As a result, diverse optimal network arrangements were obtained according to the multiple stakeholders' perspectives. This evidences that a win-win situation for all sustainability aspects considered can hardly be reached. Finally, this contribution demonstrated the applicability of the proposed methodology for sustainability evaluation, optimization, and decision-making in the context of a multi-product material facility by developing a multi-objective optimization model.

Modeling and Optimization of Recycled Water Systems to Augment Urban Groundwater Recharge through Underutilized Stormwater Spreading Basins

Infrastructure systems that use stormwater and recycled water to augment groundwater recharge through spreading basins represent cost-effective opportunities to diversify urban water supplies. However, technical questions remain about how these types of managed aquifer recharge systems should be designed; furthermore, existing planning tools are insufficient for performing robust design comparisons. Addressing this need, we present a model for identifying the best-case design and operation schedule for systems that deliver recycled water to underutilized stormwater spreading basins. Resulting systems are optimal with respect to life cycle costs and water deliveries. Through a case study of Los Angeles, California, we illustrate how delivering recycled water to spreading basins could be optimally implemented. Results illustrate trade-offs between centralized and decentralized configurations. For example, while a centralized Hyperion system could deliver more recycled water to the Hansen Spreading Grounds, this system incurs approximately twice the conveyance cost of a decentralized Tillman system (mean of 44% vs 22% of unit life cycle costs). Compared to existing methods, our model allows for more comprehensive and precise analyses of cost, water volume, and energy trade-offs among different design scenarios. This model can inform decisions about spreading basin operation policies and the development of new water supplies.

A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes

A decision support tool has been developed that uses global multiobjective optimization based on 1) the environmental impacts, evaluated within the framework of full life cycle assessment; and 2) process costs, evaluated by using rigorous process models. This approach is particularly useful in developing biorenewable-based energy solutions and chemicals manufacturing, for which multiple criteria must be evaluated and optimization-based decision-making processes are particularly attractive. The framework is demonstrated by using a case study of the conversion of terpenes derived from biowaste feedstocks into reactive intermediates. A two-step chemical conversion/separation sequence was implemented as a rigorous process model and combined with a life cycle model. A life cycle inventory for crude sulfate turpentine was developed, as well as a conceptual process of its separation into pure terpene feedstocks. The performed single- and multiobjective optimizations demonstrate the functionality of the optimization-based process development and illustrate the approach. The most significant advance is the ability to perform multiobjective global optimization, resulting in identification of a region of Pareto-optimal solutions.