Solar photovoltaic panel production in Mexico: A novel machine learning approach

This study examines the potential for widespread solar photovoltaic panel production in Mexico and emphasizes the country's unique qualities that position it as a strong manufacturing candidate in this field. An advanced model based on artificial neural networks has been developed to predict solar photovoltaic panel plant metrics. This model integrates a state-of-the-art non-linear programming framework using Pyomo as well as an innovative optimization and machine learning toolkit library. This approach creates surrogate models for individual photovoltaic plants including production timelines. While this research, conducted through extensive simulations and meticulous computations, unveiled that Latin America has been significantly underrepresented in the production of silicon, wafers, cells, and modules within the global market; it also demonstrates the substantial potential of scaling up photovoltaic panel production in Mexico, leading to significant economic, social, and environmental benefits. By hyperparameter optimization, an outstanding and competitive artificial neural network model has been developed with a coefficient of determination values above 0.99 for all output variables. It has been found that water and energy consumption during PV panel production is remarkable. However, water consumption (33.16 × 10-4 m3/kWh) and the emissions generated (1.12 × 10-6 TonCO2/kWh) during energy production are significantly lower than those of conventional power plants. Notably, the results highlight a positive economic trend, with module production plants generating the highest profits (35.7%) among all production stages, while polycrystalline silicon production plants yield comparatively lower earnings (13.0%). Furthermore, this study underscores a critical factor in the photovoltaic panel production process which is that cell production plants contribute the most to energy consumption (39.7%) due to their intricate multi-stage processes. The blending of Machine Learning and optimization models heralds a new era in resource allocation for a more sustainable renewable energy sector, offering a brighter, greener future.

Strategic planning for the optimal distribution of COVID-19 vaccines

This work presents a multi-objective optimization strategy for fair vaccine allocation through different fairness schemes. The proposed approach considers a diverse series of parameters related to different public health data and social behaviors that influence the correct distribution of vaccines, such as corruption and crime. Simultaneously, the formulation includes prioritizing those groups with the highest risk based on the epidemiological traffic light. Furthermore, the presented strategy involves different budget constraints that allow identifying trade-off solutions through Pareto fronts. Therefore, vaccine allocations are obtained by combining fairness concepts with multi-objective optimization. The applicability of the model is illustrated using the case study of Mexico. The solution to the proposed scenarios was carried out using different justice schemes and an economic objective function. The results show the compromises between a satisfaction index and costs, which are shown through Pareto optimal solutions that allow selecting the solutions that balance the objectives. The solutions provided by the social welfare scheme suggest a greater allocation of vaccines to those states with higher epidemiological risk, which may be helpful in the first stage of vaccination. On the other hand, the Rawlsian scheme provides more balanced solutions that can be useful in situations with lower rates of infection. Finally, the Nash scheme is the one that provides the most balanced solutions, favoring to a lesser extent the areas with the highest epidemiological risk, which may be useful in the later stages of vaccination.

A hybrid machine learning-mathematical programming optimization approach for municipal solid waste management during the pandemic

This paper provides a mathematical optimization strategy for optimal municipal solid waste management in the context of the COVID-19 epidemic. This strategy integrates two approaches: optimization and machine learning models. First, the optimization model determines the optimal supply chain for the municipal waste management system. Then, machine learning prediction models estimate the required parameters over time, which helps generate future projections for the proposed strategy. The optimization model was coded in the General Algebraic Modeling System, while the prediction model was coded in the Python programming environment. A case study of New York City was addressed to evaluate the proposed strategy, which includes extensive socioeconomic data sets to train the machine learning model. We found the predicted waste collection over time based on the socioeconomic data. The results show trade-offs between the economic (profit) and environmental (waste sent to landfill) objectives for future scenarios, which can be helpful for possible pandemic scenarios in the following years.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-023-03354-2.

Techno-economic analysis of the process for obtaining Bioethanol from rice husks and whey

This work proposes a novel integrated process for second-generation bioethanol production with an approach simulated in Aspen HYSYS®. Rice husk and dairy whey were used to revalorize for this bioprocess. The energy recovery of the bioprocess was optimized using the Pinch method; savings of 45.45% and 100% were obtained for heating and cooling utilities, respectively, concerning the process without a heat exchange network (HEN). It was possible to compare the costs of mutually exclusive alternatives between the process alternatives with andwithout HEN. The capital investment with HEN was similar to the process without HEN. Instead, savings by 77.8% of utility costs per year was found in the process with HEN. A differential cash flow for ten years was generated, and a positive differential net present value (NPV) was determined. Therefore, HEN is an economically convenient and environmentally friendly option since energy consumption reduction canminimize environmental damage.

A systematic approach for assessing water, energy and food security during the COVID-19 pandemic: case study of Mexico

Assessing the security of the water-energy-food nexus is a topic of great importance, which allows determining the situation of each resource to implement actions for sustainable management of these resources in today's society. For this reason, a systematic procedure is proposed to evaluate the synergies of the water-energy-food nexus in a large region that is divided into subregions that allow considering their interactions. The new procedure considers the availability, accessibility and regional interdependence of resources while annexing economic and social aspects. A composite index called the WEF Global Index is developed, which involves the WEF nexus index and has nine indicators that evaluate the availability, accessibility and regional interdependence of each resource in the water-energy-food nexus. This new index considers the Gross Domestic Product per capita and the involved population. As a case study, the 32 states of Mexico were considered to assess the effects of the COVID-19 pandemic on the economy and the security of the water-energy-food nexus at the state level. For this, the composite index was evaluated in the years 2019 and 2020. The results show that from 2019 to 2020, the value of the global index increased in 13 states, in 21 states the security index of the WEF nexus increased, and in 9 states the GDP per capita index increased. On the other hand, the results indicate that in 11 states there was no improvement in the nexus security index due to the increase in water demand, which considerably affected the water availability indicator.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02671-2.

Optimal waste management during the COVID-19 pandemic

There have been many problems generated by the COVID-19 pandemic. One of them is the worrying increase in the generation of medical waste due to the great risk they represent for health. Therefore, this work proposes a mathematical model for optimal solid waste management, proposing a circular value chain where all types of waste are treated in an intensified industrial park. The model selects the processing technologies and their production capacity. The problem was formulated as a mixed-integer linear programming problem to maximize profits and the waste processed, minimizing environmental impact. The proposed strategy is applied to the case study of the city of New York, where the increase in the generation of medical waste has been very significant. To promote recycling, different tax rates are proposed, depending on the amount of waste sent to the landfill. The results are presented on a Pareto curve showing the trade-off between profits and processed waste. We observed that the taxes promote recycling, even of those wastes that are not very convenient to recycle (from an economic point of view), favoring profits, reducing the environmental impact, and the risk to health inherent to the medical waste.

Correction to "Synthesis and Sustainability Evaluation of a Lignocellulosic Multifeedstock Biorefinery Considering Technical Performance Indicators"

[This corrects the article DOI: 10.1021/acsomega.0c00114.].

Fair Allocation of Potential COVID-19 Vaccines Using an Optimization-Based Strategy

The fair allocation of resources among multiple stakeholders in any area is a complex challenge for decision-making. This paper presents an optimization strategy for the allocation of COVID-19 vaccines, when they are available, through different fairness schemes (social welfare, Nash, Rawlsian justice, and social welfare II scheme). The applicability of the proposed model is illustrated using the case study of Mexico, including the states of the country as stakeholders. We involve several parameters to guide the allocation, such as the size, risk profiles, and fraction of vulnerable groups in the population. Furthermore, different scenarios of the availability of potential COVID-19 vaccines were evaluated. The social welfare approach is the most commonly used scheme for the allocation of resources. However, we demonstrate that this scheme yields non-unique or multiple solutions (through the social welfare II approach). These social welfare approaches provide inequalities in the allocations that become critical when resources are scarce. Specifically, the social welfare scheme favors large stakeholders (greater population) in all scenarios. We also observe how the complexity of the allocation increases with the higher availability of vaccines. Hence, it is relevant to consider allocation schemes to identify fair solutions.

Multi-objective Optimization Approach Based on Deterministic and Metaheuristic Techniques to Resource Management in Health Crisis Scenarios Under Uncertainty

This paper presents an optimization strategy based on mathematical programming to solve resource management problems regarding hospitalization of sick patients, considering emergency scenarios, such as those that can occur in a pandemic. This optimization strategy is based on the use of efficient optimization tools in solving complex problems in which other strategies are not efficient. The mathematical modeling of epidemiological phenomena is a useful tool to predict the direction of a disease, as well as to adequately and timely manage the available resources and thus save as many lives as possible. This work uses a mathematical model formulation based on deterministic optimization developed in general algebraic modeling system (GAMS) environment. The main user interface has been developed in a Microsoft™ (MS) Excel worksheet, which is familiar to many users. The linking code to send values from MS Excel to GAMS has been programmed in visual basic for applications (VBA) and it uses GAMS data exchange (GDX) files. The proposed optimization methodology is applied to case studies based on data obtained from affected people by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the virus that causes coronavirus disease 2019 (COVID-19). The distances were taken as example from severely affected cities in the USA. The obtained results offer attractive alternatives for the specified objective function in an acceptable computation time.

Optimization of municipal solid waste management using a coordinated framework

Municipal solid waste (MSW) management is an important but complex logistical problem. The deployment of MSW management systems is hindered by the ever-growing generation of waste and the often insufficient infrastructure to manage, process, and dispose of waste. This paper presents a coordinated framework for complex MSW management systems. The framework accommodates multiple key stakeholders in MSW systems, such as suppliers of waste, consumers of waste and derived products, and providers of transportation and processing services. Here, the stakeholders submit bids to a coordinator that solves an optimization problem to determine allocations and clearing prices that maximize the collective profit for all stakeholders and that balance supply and demand for waste and products. Furthermore, the clearing process guarantees that the individual profits are non-negative (no stakeholder loses money). Notably, the framework operates as a competitive market that accelerates transactions between stakeholders and that handles complex logistical constraints that would be difficult to handle in peer-to-peer transactions. The framework also facilitates the integration of policy incentives and the monetization of environmental impacts. In this regard, we evaluate a tax applied to open dump disposal. To illustrate the applicability, an MSW system in Mexico was analyzed as a case study. Results reveal that taxation can be used to incentivize the provision of services for all stakeholders. Specifically, we found that an appropriate tax can completely avoid disposal in open dumps. A tax of 5.1 USD/tonne was identified as the minimum penalization that avoids diverting waste to open dumps.

Synthesis and Sustainability Evaluation of a Lignocellulosic Multifeedstock Biorefinery Considering Technical Performance Indicators

Nowadays, green-chemistry principles offer an approach that fits to ensure chemical process sustainability by the use of low-cost renewable raw materials, waste prevention, inherent safer designs, among others. Based on this motivation, this study presents a novel methodology for sustainable process design that comprises the synthesis of a multifeedstock optimal biorefinery under simultaneous optimization of economic and environmental targets and further sustainability evaluation using the sustainability weighted return on investment metric (SWROIM). The first step of the proposed method is the formulation of an optimization model to generate the most suitable process alternatives. The model took into account various biomasses as available raw materials for production of ethanol, butanol, succinic acid, among others. Process technologies such as fermentation, anaerobic digestion, gasification, among others, were considered for biorefinery design. Once the model synthesizes the optimal biorefinery, we used environmental, safety, economic, and energy analyses to assess the process, which is a case study for north Colombia. Process simulation generated the data needed (extended mass and energy balances, property estimation, and modeling of downstream) to develop the process analysis stage via the Aspen Plus software. Results for the environmental and economic analyses showed that the assumption considered to solve the optimization problem was adequate, yielding promising environmental and economic outcomes. Finally, the overall sustainability evaluation showed a SWROIM of 27.29%, indicating that the case study showed higher weighted performance compared to the return on investment (ROI) metric of 14.33%.

A multi-objective optimization approach for the selection of working fluids of geothermal facilities: Economic, environmental and social aspects

The selection of the working fluid for Organic Rankine Cycles has traditionally been addressed from systematic heuristic methods, which perform a characterization and prior selection considering mainly one objective, thus avoiding a selection considering simultaneously the objectives related to sustainability and safety. The objective of this work is to propose a methodology for the optimal selection of the working fluid for Organic Rankine Cycles. The model is presented as a multi-objective approach, which simultaneously considers the economic, environmental and safety aspects. The economic objective function considers the profit obtained by selling the energy produced. Safety was evaluated in terms of individual risk for each of the components of the Organic Rankine Cycles and it was formulated as a function of the operating conditions and hazardous properties of each working fluid. The environmental function is based on carbon dioxide emissions, considering carbon dioxide mitigation, emission due to the use of cooling water as well emissions due material release. The methodology was applied to the case of geothermal facilities to select the optimal working fluid although it can be extended to waste heat recovery. The results show that the hydrocarbons represent better solutions, thus among a list of 24 working fluids, toluene is selected as the best fluid.