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Research article

Global sensitivity analysis and uncertainty quantification for a mathematical model of dry anaerobic digestion in plug-flow reactors

  • In many applications, complex biological phenomena can be reproduced via structured mathematical models, which depend on numerous biotic and abiotic input parameters, whose effect on model outputs can be of paramount importance. The calibration of model parameters is crucial to obtain the best fit between simulated and experimental data. Sensitivity analysis and uncertainty quantification constitute essential tools in the field of biological systems modeling. Despite the significant number of applications of sensitivity analysis in wet anaerobic digestion, there are no examples of global sensitivity analysis for mathematical models describing the dry anaerobic digestion in plug-flow reactors. For the first time, the present study explores the global sensitivity analysis and uncertainty quantification for a plug-flow reactor model. The investigated model accounts for the mass/volume variation that takes place in these systems as a result of solid waste conversion in gaseous value-added compounds. A preliminary screening based on the Morris' method allowed for the definition of three different groups of parameters. A surrogate model was constructed to investigate the relation between input and output parameters without running demanding simulations from scratch. The obtained Sobol' indices allowed to perform the quantitative global sensitivity analysis. Finally, the uncertainty quantification results led to the definition of the probability density function related to the investigated quantity of interest. The study showed that the net methane production is mostly sensitive to the values of the conversion parameter related to the particulate biodegradable volatile solids in acetic acid k1 and to the kinetic parameter describing the acetic acid uptake k2. The application of these techniques led to helpful information for model calibration and validation.

    Citation: Daniele Bernardo Panaro, Andrea Trucchia, Vincenzo Luongo, Maria Rosaria Mattei, Luigi Frunzo. Global sensitivity analysis and uncertainty quantification for a mathematical model of dry anaerobic digestion in plug-flow reactors[J]. Mathematical Biosciences and Engineering, 2024, 21(9): 7139-7164. doi: 10.3934/mbe.2024316

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  • In many applications, complex biological phenomena can be reproduced via structured mathematical models, which depend on numerous biotic and abiotic input parameters, whose effect on model outputs can be of paramount importance. The calibration of model parameters is crucial to obtain the best fit between simulated and experimental data. Sensitivity analysis and uncertainty quantification constitute essential tools in the field of biological systems modeling. Despite the significant number of applications of sensitivity analysis in wet anaerobic digestion, there are no examples of global sensitivity analysis for mathematical models describing the dry anaerobic digestion in plug-flow reactors. For the first time, the present study explores the global sensitivity analysis and uncertainty quantification for a plug-flow reactor model. The investigated model accounts for the mass/volume variation that takes place in these systems as a result of solid waste conversion in gaseous value-added compounds. A preliminary screening based on the Morris' method allowed for the definition of three different groups of parameters. A surrogate model was constructed to investigate the relation between input and output parameters without running demanding simulations from scratch. The obtained Sobol' indices allowed to perform the quantitative global sensitivity analysis. Finally, the uncertainty quantification results led to the definition of the probability density function related to the investigated quantity of interest. The study showed that the net methane production is mostly sensitive to the values of the conversion parameter related to the particulate biodegradable volatile solids in acetic acid k1 and to the kinetic parameter describing the acetic acid uptake k2. The application of these techniques led to helpful information for model calibration and validation.



    Clarke's subdifferential operator is associated with a type of nonlinear inclusion known as hemivariational inequalities. These inequalities have applications in structural analysis and non-convex optimization. The two main types of inequality problems are variational inequalities and hemivariational inequalities. Hemivariational inequalities handle nonsmooth, nonconvex energy functions, whereas variational inequalities primarily deal with convex energy functions. In 1981, Panagiotopoulos introduced the concept of hemivariational inequality as a method to represent mechanical obstacles. Hemivariational inequalities or subdifferential inclusions can be used to model various nonsmooth contact mechanics issues involving multivalued and nonmonotone constitutive laws with boundary conditions (see [1,2,3]). Since then, numerous researchers have made significant contributions to the field of hemivariational inequalities, as seen in [4,5,6,7,8,9]. Moreover, neutral differential systems with impulsive effects have become a prominent area of research, modeling real-world processes that undergo sudden changes at specific points. This field has far-reaching applications in areas like finance, economics, mechanics, neural networks, electronics, and telecommunications. Notably, the authors of [10] investigated the controllability of nonlocal neutral differential inclusions with impulse effects. Researchers looked at the existence of impulsive multivalued neutral functional differential inclusions [11,12].

    Research on controllability in hemivariational-type systems has gained significant attention from the scientific community in recent times. Control problems have significant implications for various fields, including engineering, physics, and finance [13]. Despite the progress made, many intriguing questions and concepts remain unexplored. Notably, the authors of [14,15] established the existence of optimal control in hemivariational inequalities, while [16,17] investigated optimal control in parabolic hemivariational inequalities. Furthermore, [18,19] demonstrated the existence of optimal control in hyperbolic systems, contributing to the advancement of the field. Due to their wide application to numerous pragmatist mathematics fields, neutral systems have attracted attention recently. Neutral systems have numerous applications in various fields, including thermal expansion of materials, biological advancements, surface waves, and stretchability, which benefit from neutral systems either directly or indirectly. Additionally, researchers have extensively studied hemivariational inequalities with a neutral type in [20,21]. For further information regarding the system with hemivariational inequalities, refer to [22,23,24,25,26]. Recently, there has been a surge of interest in Clarke's subdifferential evolution inclusion problems, particularly in the context of nonsmooth analysis and optimization. Frictional contact analysis can effectively characterize the interaction between a piezoelectric body and an electrically conducting foundation, and the frictional contact between a piezoelectric cylinder and a foundation exhibits anti-plane shear deformations. Moreover, the authors of [27] have established approximate controllability results for Sobolev-type Hilfer fractional neutral evolution problems with Clarke's subdifferential-type problems. Our proposed problem presents a model that combines the key elements of hemivariational inequality and fractional impulsive differential equations into a unified framework. This study establishes approximate controllability results for a neutral differential system with impulsive effects, formulated as a hemivariational inequality. Next, we will define the specific system under consideration, which will be the focus of our investigation:

    {ddφ[u(φ)g1(φ,u(φ))]+A(φ)u(φ)+Bv(φ),ωM+H(φ,u(φ);ω)0,φiφL=[0,a],  ωM,u(0)=u0, u(0)=u1,Δu(φi)=Li(u(φi)),Δu(φi)=Ki(u(φi)), i=1,2,3,...,m, 0<φ1<φ2<...<φm<a. (1.1)

    Here ,M stands for the inner product of the separable Hilbert space M and A:D(A)MM is a closed, linear, and densely defined operator on M. H(φ,;) denotes the generalized Clarke's directional derivative [6] of a locally Lipschitz function H(φ,):MR. The function g1:L×MM. The control function vV takes values from L2(L,V) and V is the control set which is also a Hilbert space. Let B:VM be the bounded linear operator. Let 0<φ1<φ2<...<φm<φm+1=a be pre-fixed points and the symbol Δu(φi) represents the jump in the state u at time t which is defined by Δu(φi)=u(φ+i)u(φi), and u(φi) and u(φ+i) denote the left and right limit of u at φi. Li:MM and Ki:MM,i=1,2,3,...,m, are impulsive functions.

    The structure of the article is presented in the following way:

    ● We begin by reviewing key definitions, fundamental theorems, and initial findings from a previous section.

    ● The core focus then shifts to exploring the existence of mild solutions for the system represented by Eq (1.1).

    ● Next, we investigate the approximate controllability of the system (1.1).

    ● Finally, we solidify our theoretical results by presenting a concrete example.

    Let Z and Z be a Banach space and its dual, respectively with Z and , is the duality pairing of Z and Z. The Banach space PC(L,Z) is the set of all piecewise continuous functions from L=[0,a] into Z together with uPC=supφLu(φ)Z. Also,

    M(w)k(e)(Z):={Z: is (weakly) compact (convex)};Mf(e)(Z):={Z: is closed (convex)}.

    Consider the non-autonomous second-order initial value problem:

    z(φ)= A(φ)z(φ)+f(φ),0z,φa, (2.1)
    z(z)= z0,z(z)=z1, (2.2)

    where A(φ):D(A(φ))MM, φ[0,a] is a closed densely defined operator and f:[0,a]M is an appropriate function. One can refer to [15,28,29,30] and the references therein. A significant number of articles relate the existence of solutions to the (2.1)-(2.2) problem to the existence of the evolution operator Q(φ,z) for the homogeneous equation

    z(φ)= A(φ)z(φ),0φa. (2.3)

    Assume that the domain of A(φ) is a subspace D that is dense in M and independent of φ, and that the function φA(φ)z is continuous for each zD(A(φ)).

    In view of Kozak's work [31], we shall apply the following evolution operator notion in this study.

    Definition 2.1. A family Q of bounded linear operators Q(φ,z):[0,a]×[0,a]L(M) is called an evolution operator for (2.3) if the following conditions are satisfied:

    (Z1) For each zM, the mapping [0,a]×[0,a](φ,z)Q(φ,z)zM is of class C1 and

    (i) for each φ[0,a], Q(φ,φ)=0,

    (ii) for all φ,z[0,a], and for each zM,

    φQ(φ,z)z|φ=z=z,zQ(φ,z)z|φ=z=z.

    (Z2) For all φ,z[0,a], if zD(A), then Q(φ,z)zD(A), the mapping [0,a]×[0,a](φ,z)Q(φ,z)zM is of class C2 and

    (i) 2φ2Q(φ,z)z=A(φ)Q(φ,z)z,

    (ii) 2z2Q(φ,z)z=Q(φ,z)B(z)z,

    (iii) zφQ(φ,z)z|φ=z=0.

    (Z3) For all φ,z[0,a], if zD(A), and then zQ(φ,z)zD(A), then 2φ2zQ(φ,z)z, 2z2φQ(φ,z)z, and

    (i) 2φ2zQ(φ,z)z=A(φ)zQ(φ,z)z,

    (ii) 2z2φQ(φ,z)z=φQ(φ,z)A(z)z,

    and the mapping [0,a]×[0,a](φ,z)A(φ)zQ(φ,z)z is continuous.

    Let us take

    C(φ,z)=Q(φ,z)z.

    Also, for some positive constants N1 and N2, we set sup0φ,zaQ(φ,z)N2 and sup0φ,zaC(φ,z)N1 and

    Q(φ+g,z)Q(φ,z)N1|g|, (2.4)

    for all z,φ,φ+g[0,a]. The mild solution z:[0,a]M of (2.1)-(2.2) is:

    z(φ)=C(φ,z)z0+Q(φ,z)z1+φ0Q(φ,η)f(η)dη.

    Let us start by discussing the necessary definitions and findings from the multivalued analysis. The following sources [7,32,33] are recommended for readers in addition to multivalued maps.

    Definition 2.2. Given a Banach space Z and a multivalued map H:Z2Z{}=N(Z), we say

    (i) H is convex and closed valued, only when H(u) is convex and closed valued for all uZ.

    (ii) H is said to be upper semicontinuous on Z, if for all yZ, H(u) is closed in Z and if for each open set J1 of Z which contains H(u), then there is an open neighborhood E of u such that H(E)J1.

    (iii) H is bounded on bounded sets if H(B)=uBH(u) is bounded in Z for all BMb(Z) (i.e., supuB{sup{k:kH(u)}}<).

    (iv) H is supposed to be completely continuous provided that H(J1) is relatively compact, for all bounded subset J1Z.

    (v) H has a fixed point if there is a uZ such that uH(u).

    For a locally Lipschitzian functional H:ZR, we denote H0(q;p), the Clarke's generalized directional derivative of H at point q in the direction of p, that is,

    H0(q;p):=limγ0+supϑqH(ϑ+γp)H(ϑ)γ.

    Also, H(q):={qZ:H0(q;p)q;p,for every pZ} denotes the generalized Clarke's subdifferential.

    The subsequent features and outcomes will facilitate our goal achievement:

    Lemma 2.3. [9] If the function H:R is a locally Lipschitz on an open set of Z, then

    (i) For all pZ, it has H0(q;p)=max{q;p: for all qH(q)};

    (ii) For all q, the gradient H(q) is a nonempty, convex, weak-compact subset of Z and qZM, for all qH(q);

    (iii) The graph of H is closed in ×Z. That is, if {qn}, {qn}Z are sequences as qnH(qn) and qnqZ, qnq weakly in Z, then qH(q) (where the Banach space Z furnished with the -topology is denoted by Z);

    (iv) The multifunction qH(q)Z is upper semicontinuous which maps into Z.

    Lemma 2.4. [9] Let Z be the separable reflexive Banach space, 0<a< and H:(0,a)×ZR, such that  H(,w) is measurable for each wZ and H(φ,) is locally Lipschitz on Z for all φ(0,a). Then the multifunction (0,a)×Z(φ,w)H(φ,w)Z is measurable.

    As discussed in [9], we can investigate the existence of mild solutions and approximate controllability for the following semilinear inclusions:

    {ddφ[z(φ)g1(φ,u(φ))]A(φ)u(φ)+Bv(φ)+H(φ,z(φ)), φL=[0,a],u(0)=u0, u(0)=u1,Δu(φi)=Li(u(φi)), Δu(φi)=Ki(u(φi)), i=1,2,3,...,m. (2.5)

    Now, we can explore the implication that every solution to Eq (2.5) is also a solution to Eq (1.1). Hence, if uPC(L,M) is a solution of (2.5), there exists h(φ)H(φ,u(φ)) provided hL2(L,M) and

    {ddφ[u(φ)g1(φ,u(φ))]=A(φ)u(φ)+Bv(φ)+h(φ), φL=[0,a],u(0)=u0, u(0)=u1,Δz(φi)=Li(u(φi)), Δu(φi)=Ki(u(φi)), i=1,2,3,...,m,

    which implies

    {ddφ[u(φ)g1(φ,u(φ))]+A(φ)u(φ)+Bv(φ),ωM+h(φ),ωM=0, φL=[0,a],for all ωM,u(0)=u0, u(0)=u1,Δz(φi)=Li(u(φi)), Δu(φi)=Ki(u(φi)), i=1,2,3,...,m.

    Since h(φ),ωMH0(φ,u(φ);ω) and h(φ)H(φ,u(φ)),

    {ddφ[u(φ)g1(φ,u(φ))]+A(φ)u(φ)+Bv(φ),ωM+ H0(φ,u(φ);ω), φL=[0,a], for all ωM,u(0)=u0, u(0)=u1,Δu(φi)=Li(u(φi)), Δu(φi)=Ki(u(φi)), i=1,2,3,...,m.

    Therefore, our initial focus will be on examining the semilinear inclusion (2.5), which proceeds our investigation of the hemivariational inequality (1.1). According to established literature [33,34], the mild solution for problem (2.5) is defined as below.

    Definition 2.5. For all vL2(L,V), a function uPC(L,M) is a mild solution for (2.5) if there exists hL2(L,M) as h(φ)H(φ,u(φ)) almost everywhere on φL,

    u(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)), φL.

    Let us take the following assumptions:

    (A1) The function H:L×MR satisfies:

    (i) uM, φH(φ,z) is measurable;

    (ii) uH(φ,u) is locally Lipschitz continuous for a.e. φL;

    (iii) There is a function b(φ)L2(L,R+) and e>0 such that

    H(φ,u)=sup{h:h(φ)H(φ,u)}b(φ)+eu,for a.e. φL and for each uM.

    (A2) In the function g1:L×MM, there exists some constants C1,C2>0, and for every uM, φ1,φ2L, we have

    g1(φ1,u(φ1))g1(φ2,u(φ2))C1u(φ1)u(φ2),g1(φ1,u(φ1))C2(1+u(φ1)).

    (A3) For some constants ci,li>0, the maps Li,Ki:MM are continuous, and

    Li(φ)ci,Ki(φ)li, i=1,2,3,...,m, for all uM.

    Let M:L2(L,M)2L2(L,M) be defined as below:

    M(u)={kL2(L,M):k(φ)H(φ,u(φ)) almost everywhere, φL}, for all uL2(L,M).

    Lemma 2.6. [35] Let (A1) and M be true. If unu in L2(L,M), wnw weakly in L2(L,M) and unM(un), and then uM(u).

    Lemma 2.7. [9] Let all hypotheses and (A1) hold. Then for every uL2(L,M), the set M(u) has nonempty, weakly compact and convex values.

    Theorem 2.8. [9] Consider the Banach space Z which is locally convex and Υ:Z2Z is a compact convex valued, upper semicontinuous multivalued map such that there exists a closed neighborhood J of 0 for which Υ(J) is relatively compact provided

    ={xZ:γxΥ(u), for some γ>1}

    is bounded. Then Υ has a fixed point.

    Theorem 3.1. For all vL2(L,V), provided that (A1)(A3) are fulfilled, then (2.5) has a mild solution on L such that a(N1C2+N2e)<1, where N1:=supφ,z[0,a]C(φ,z), N2:=supφ,z[0,a]Q(φ,z).

    Proof. Initially, choose any uPC(L,M)L2(L,M), by Lemma 2.7. Now, define Υ:PC(L,M)2PC(L,M) as:

    Υ(u)={fPC(L,M):f(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)), hM(u)}, uPC(L,M).

    It is clear that we can determine a fixed point of the multivalued map Υ that satisfies Theorem 2.8. First, note that the set-valued map Υ(u) is convex due to the properties of M(u). Now, let us proceed with the proof of the theorem as follows:

    Step 1. Υ(Br)(Br),r>0, is bounded in PC(L,M), where Br={uPC(L,M):uPCr}. Here, it suffices to demonstrate the existence of a positive constant such that for each σΥ(u), uBr,σPC. If σΥ(u), then there is a hM(u) provided

    σ(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)),φL.

    By Hölder's inequality,

    σ(φ)C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φC(φ,φi)Ki(u(φi))N1u0+N2[u1g1(0,u(0))]+N1φ0C2(1+r)dz+N2φ0[b(z)+er+Bv(z)]dz+N10<φi<φLi(u(φi))+N20<φi<φKi(u(i))N1u0+N2φ2[u1g1(0,u(0))]+N1aC2(1+r)+N2[abL2(Q,R+)+era+aBvL2(Q,V)]+N1mi=1ci+N2mi=1li:=.

    Thus, Υ(Br) is bounded.

    Step 2. {Υ(u):uBr} is completely continuous.

    Let us note that for any uBr, σΥ(u), there exists hM(u) such that for all φL,

    σ(φ)C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φC(φ,φi)Ki(u(φi)).

    For 0<ξ1<ξ2a and k>0 very small,

    σ(ξ2)σ(ξ1)MC(ξ2,0)C(ξ1,0)u0+Q(ξ2,0)Q(ξ1,0)[u1g1(0,u(0))]+ξ1k0C(ξ2,z)C(ξ1,z)g1(z,u(z))dz+ξ1ξ1kC(ξ2,z)C(ξ1,z)g1(z,u(z))dz+ξ2ξ1C(ξ2,z) g1(z,u(z))dz+ξ1k0Q(ξ2,z)Q(ξ1,z)h(z)+Bv(z)dz+ξ1ξ1kQ(ξ2,z)Q(ξ1,z) h(z)+Bv(z)dz+ξ2ξ1Q(ξ2,z) h(z)+Bv(z)dz+0<φi<aC(ξ2,φi)C(ξ1,φi)Li(u(φi))+0<φi<aQ(ξ2,φi)Q(ξ1,φi)Ki(u(φi))C(ξ2,0)C(ξ1,0)u0+Q(ξ2,0)Q(ξ1,0)[u1g1(0,u(0))]+ξ1k0C(ξ2,z)C(ξ1,z)C2(1+r)dz+ξ1ξ1kC(ξ2,z)C(ξ1,z)C2(1+r)dz+N1 C2(1+r)(ξ2ξ1)+ξ1k0Q(ξ2,φi)Q(ξ1,φi)[b(z)+er+Bv(z)]dz+ξ1ξ1kQ(ξ2,z)Q(ξ1,z)[b(z)+er+Bv(z)]dz+N2ξ2ξ1[b(z)+er+Bv(z)]dz+0<φi<aC(ξ2,φi)C(ξ1,φi)Li(u(φi))+0<φi<aQ(ξ2,φi)Q(ξ1,φi)Ki(u(φi)). (3.1)

    From the uniform operator topology [33, Lemma 6.2], it is easily understood that (3.1) tends to zero of uBr as ξ2ξ1 and k0.

    Equivalently, for ξ1=0 and 0<ξ2a, we can show that σ(ξ2)u0M tends to zero independently of uBr as ξ20. Hence, we can conclude that {Υ(u):uBr} is equicontinuous of PC(L,M).

    Finally, from the assumptions (A1) and (A3) and by the definition of a relatively compact set, it is not difficult to check that {σ(t):σΥ(Br)} is relatively compact in M. Thus, by the generalized Arzelˊa-Ascoli theorem, we get that Υ is a multivalued compact map.

    Therefore, based on the above arguments, Υ is completely continuous.

    Step 3. Assume unu in PC(L,M), σnΥ(un) and σnσ in PC(L,M). Let us check σΥ(u). It is obvious that σnΥ(un) exist only when hnM(un) such that

    σn(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,un(z))dz+φ0Q(φ,z)hn(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)). (3.2)

    Here {hn}n1L2(L,M) is bounded from the hypothesis (A2). Hence we may assume that

    hnh weakly in L2(L,M). (3.3)

    From (3.2) and (3.3), we have

    σn(φ)C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)). (3.4)

    We can see that σnσ in PC(L,M) and hnM(u). According to Lemma 2.6 and (3.4), hM(u). Then, σΥ(u), and this shows that Υ has a closed graph. Hence using Proposition 3.12 of [36] implies that it is upper semicontinuous.

    Step 4. A priori estimate.

    Based on the results from Steps 1–3, we have established that the multivalued map Υ satisfies the following properties: upper semicontinuity, convex-valuedness, compactness, and relative compactness of Υ(Br). Therefore, Υ meets the conditions of Theorem 2.8, which implies that

    ={uPC(L,M):γuΥ(u), γ>1},

    is bounded. To prove Υ has a fixed point, let u, and hM(u) such that

    u(φ)=γ1C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+γ1φ0C(φ,z)g1(z,u(z))dz+γ1φ0Q(φ,z)h(z)dz+γ1φ0Q(φ,z)Bv(z)dz+γ10<φi<φC(φ,φi)Li(u(φi))+γ10<φi<φQ(φ,φi)Ki(u(φi)).

    From our assumptions,

    u(φ)MC(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi))N1u0+N2[u1g1(0,u(0))]+N1φ0C2(1+u(z))dz+N2φ0[a(z)+eu(z)+Bv(z)]dz+N10<φi<φLi(u(φi))+N20<φi<φKi(u(φi))ρ+K1u, (3.5)

    where

    ρ=N1[u0]+N2[u1g1(0,u(0))]+N1C2b+N2(bL2(L,R+)+BvL2(L,V))a+mi=1li]+N1mi=1ci,K1=a(N1C2+N2e).

    Hence, by the assumption K1<1 and (3.5), we can see that

    u=supφLu(φ)ρ+K1u, thus uρ1K1=:2.

    Hence, Υ has a fixed point.

    Consider that the mild solution for the Eq (2.5) is u(;v), the control variable v has values in L2(L,V), and the initial value is u0,u1M. At the terminal time a, the accessible set of the system (2.5) is defined as R(a,u0,u1)={u(a;u0,u1):vL2(L,V)}.

    Definition 4.1. The Eq (2.5) is approximately controllable on L, if for any initial value u0,u1M, then ¯R(a,u0,u1)=M.

    Consider the linear differential system:

    {u(φ)=A(φ)u(φ)+Bv(φ), φL=[0,a],u(0)=u0,u(0)=u1. (4.1)

    Now, define the operators for the system (4.1) as:

    Υa0=a0Q(a,z)BBQ(a,z)dzandR(β,Υa0)=(βI+Υa0)1, β>0,

    where B and Q(φ) are adjoint of B and Q(φ), respectively.

    Lemma 4.2. [9] The system (4.1) is approximately controllable on L iff β R(β,Υa0)0 as β0+ in the strong operator topology.

    Choose any β>0, uPC(L,M)L2(L,M) and uaM, as stated in Lemma 2.7, and it is possible to define a multivalued map Υβ:PC(L,M)2PC(L,M) given by

    Υβ(u)={fPC(L,M):f(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)),hM(u)},

    and

    vβ(φ)=BQ(a,z)R(β,Υa0)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,z)g1(z,u(z))dza0Q(a,z)h(z)dz0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi))).

    Theorem 4.3. Let (A1)(A3) be true. Υβ, for all β>0, has a fixed point on L=[0,a] if

    a(N1C2+N2e)(1+N22B2β)<1,

    where N1:=supφ,z[0,a]C(φ,z), N2:=supφ,z[0,a]Q(φ,z).

    Proof. For every uPC(L,M), by the nature of M(u), we can say Υβ is convex.

    Step 1. For every p>0,Υβ(Bp) is bounded in PC(L,M),

    Bp={uPC(L,M):uPCp}.

    Here, it is sufficient to prove that there exists a positive constant lβ and for all σΥβ(u), uBp, σPClβ. If σΥβ(u), there is hM(u) such that

    σ(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)), φL.

    Notice that

    vβ(φ)=BQ(a,z)R(β,Υa0)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,z)g1(z,u(z))dza0Q(a,z)h(z)dz0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)))N2Bβ[ua+N1u0+N2u1g1(0,u(0))+aN1C2(1+p)+N2[bL2(L,R+)a+epa]+N1mi=1ci+N2mi=1li]:=Ψ. (4.2)

    From (4.2),

    σ(φ)MC(φ,0)u0M+Q(φ,0)[u1g1(0,u(0))]M+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φC(φ,φi)Ki(u(φi))N1u0+N2[u1g1(0,u(0))]+N1aC2(1+p)+N2[abL2(L,R+)+epa+BΨa]+N1mi=1ci+N2mi=1li:=lβ.

    Thus Υβ(Bp) is bounded in PC(L,M).

    Step 2. Consider any uBp, σΥβ(u). There exists hM(u), for every φL,

    σ(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bv(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)).

    Using vβ(t) as (4.2) and also from Theorem 3.1, Step 2, one can obtain that {Υβ(u):uBp} is completely continuous.

    Step 3. Consider unu in PC(L,M), σnΥβ(un) and σnσ in PC(L,M). We investigate σΥβ(u). Indeed, σnΥβ(un) exists only when hnM(un) such that

    σn(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,un(z))dz+φ0Q(φ,z)hn(z)dz+φ0Q(φ,z)BBQ(a,z)R(β,Υa0)(×)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,η)g1(η,un(η))dηa0Q(a,η)hn(η)dη0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)))dz+0<φi<φC(φ,φi)Li(u(φi))+0<ti<tQ(φ,φi)Ki(u(φi)). (4.3)

    From (A1), we will prove {hn}n1L2(L,M) is bounded. Hence,

    hnh weakly in L2(L,M). (4.4)

    From (4.3) and (4.4),

    σn(φ)C(φ,0)u0+Q(φ,0)[z1g1(0,z(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)BBQ(a,z)R(β,Υa0)(×)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,η)g1(η,u(η))dηa0Q(a,η)h(η)dη0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)))dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)). (4.5)

    Clearly, σnσ in PC(L,M) and hnM(un). According to Lemma 2.6 and (4.5), hM(u). Then, σΥ(u), and this shows that Υ has a closed graph. Hence, by using Proposition 3.12 of [36] implies that it is upper semicontinuous.

    Step 4. A priori estimate.

    By Steps 1–3, Υβ is convex valued, compact upper semicontinuous, Υβ(Bp) is a relatively compact set and meets Theorem 2.8, and

    J={uPC(L,M):γuΥβ(u), γ>1},

    is bounded.

    Consider uJ. Then there exists hM(u) such that

    u(φ)=γ1C(φ,0)u0+γ1Q(φ,0)[u1g1(0,u(0))]+γ1φ0C(φ,z)g1(φ,u(φ))dz+γ1φ0Q(φ,z)h(z)dz+γ1φ0Q(φ,z)Bvβ(z)dz+γ10<φi<φC(φ,φi)Li(u(φi))+γ10<φi<φQ(φ,φi)Ki(u(φi)),

    and

    vβ(φ)=BQ(a,z)R(β,Υa0)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,z)g1(z,u(z))dza0Q(a,z)h(z)dz0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi))).

    Then from our assumptions,

    u(φ)MC(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)h(z)dz+φ0Q(φ,z)Bvβ(z)dz+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi))N1z0+N2[u1g1(0,u(0))]+N1φ0C2(1+u(φ))dz+N2φ0[b(z)+eu(z)+B(N2Bβ[ua+N1u0+N2u1g1(0,z(0))+aN1C2(1+p)+N2[bL2(L,R+)a+epa]+N1mi=1ci+N2mi=1li])dz+N10<φi<φLi(u(φi))+N20<φi<φKi(u(φi))ρ+K2u, (4.6)

    where

    ρ=N1u0+N2[u1g1(0,u(0))]+N1C2a+N2[bL2(L,R+)]a+N22B2β(ua+N1u0+N2[u1g1(0,u(0))]+N1C2a+N2[bL2(L,R+)]a)+N1mi=1ci+N2mi=1li.K2=a(N1C2+N2e)(1+N22B2β).

    According to K2<1 and (4.6), we conclude,

    u=supφLu(φ)ρ+K2u, and thus uρ1K2=:3.

    Therefore J is bounded which leads to the conclusion that Υβ has a fixed point.

    Theorem 4.4. Suppose the conditions of the above theorem are satisfied. Then, if system (4.1) is approximately controllable on the set L, it follows that system (2.5) is also approximately controllable on L.

    Proof. By Theorem 4.3, Υβ, for all β>0, has a fixed point in PC(L,M). Let uβ be a fixed point of Υβ in PC(L,M). Clearly, Υβ is a mild solution of (2.5). Then, there exists hβM(uβ) such that for each φL,

    uβ(φ)=C(φ,0)u0+Q(φ,0)[u1g1(0,u(0))]+φ0C(φ,z)g1(z,u(z))dz+φ0Q(φ,z)hβ(z)dz+φ0Q(φ,z)BBQ(a,z)R(β,Υa0)(×)(uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,η)g1(η,u(η))dηa0Q(a,η)hβ(η)dη0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)))du+0<φi<φC(φ,φi)Li(u(φi))+0<φi<φQ(φ,φi)Ki(u(φi)).

    Since IΥa0R(β,Υa0)=βR(β,Υa0), we have uβ(a)=uaβR(β,Υa0)E(hβ). From the above,

    E(hβ)=uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,η)g1(η,u(η))dηa0Q(a,η)hβ(η)dη0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)).

    From the hypothesis (A1) and from Theorem 4.3, H(φ,u)b(φ)+eu(φ)b(φ)+ep:=ν(φ). Then,

    a0hβ(z)dzνL2(L,R+)a.

    Consequently {hβ} is a bounded sequence in L2(L,M). So, there exists a subsequence, {hβ}, which will converge weakly to h in L2(L,M). It is expressed as

    g=uaC(a,0)u0Q(a,0)[u1g1(0,u(0))]a0C(a,η)g1(η,u(η))dηa0Q(a,η)h(η)dη0<φi<aC(a,φi)Li(u(φi))0<φi<aQ(a,φi)Ki(u(φi)).

    Now,

    E(hβ)g=a0C(a,η)[g1(η,yβ(η))g1(η,y(η))]dη+a0S(a,η)[hβ(η)h(η)]dηM1sup0ηa[g1(η,yβ(η))g1(η,y(η))]+M2sup0ηa[hβ(η)h(η)]. (4.7)

    From Step 2 in Theorem 4.3 and by the Arzelˊa-Ascoli theorem, we get that the compactness of the right-hand side of (4.7) tends to zero as β0+, which gives

    uβ(a)u1=βR(β,Υa0)E(hβ)βR(β,Υa0)(g)+E(hβ)g0, as β0+.

    Hence, (2.5) is approximately controllable on L.

    We utilize our theoretical findings on a concrete partial differential equation. We need to provide the required technological resources to accomplish our goals.

    Now, let us take

    A(φ)=A+˜A(φ),

    where A is the infinitesimal generator of a cosine function C(φ) with associated sine function Q(t), and ˜A(φ):D(˜A(φ))M is a closed linear operator with DD(˜A(φ)), for all φL. We take the space M=L2(T,C), where the group T is defined as the quotient R/2πZ, and we denote by L2(T,C) the space of 2π periodic 2-integrable functions from R to C. Also, we use the identification between functions on T and 2π periodic functions on R. Furthermore, H2(T,C) denotes the Sobolev space of 2π periodic from R to C such that uL2(T,C).

    We define Au(φ)=u(φ) with domain D(A)=H2(T,C). Then, A can be written as

    Au=n=1n2u,xnxn,uD(A),

    where xn(φ)=12πeinφ(nZ) is an orthonormal basis of M. It is well known that A is the infinitesimal generator of a strongly continuous cosine function C(φ) on M. The cosine function C(φ) is given by

    C(φ)u=n=1cosntu,xnxn,uM,φR.

    The connected sine operator (Q(φ))φR is

    Q(t)u=n=1sinntnu,xnxn,uM,φR.

    It is clear that C(φ)1 for all φR, so it is uniformly bounded on R.

    Assume the following second-order non-autonomous neutral differential system of the form:

    φ[φu(φ,)ˆg1(φ,y(φ,)]=2q2u(φ,)+B(φ)φu(φ,)+B˜v(φ,)+ϕ(φ,u(φ,)),0<φ<a,u(φ,0)=u(φ,π)=0, 0<φ<a,u(0,)=u0(),(0,π),φy(0,)=u1(),Δu(φk)()=φk0bk(φkφ)u(φ,)dφ,Δφu(φk)()=φk0bk(φkφ)u1(φ,)dφ, (5.1)

    where B:RR is a continuous function such that supφ[0,a]B(φ)=c0, and u(φ,) represents the temperature at (0,π) and φ(0,a). Let ϕ(φ,u(φ,))=ϕ1(ξ1,φ(φ,))+ϕ2(φ,u(φ,)) and ϕ2(φ,u(φ,)) is the temperature function of the form ϕ2(φ,u(φ,))H(φ,,u(φ,)),(φ,)(0,a)×(0,π). Here, the nonsmooth and nonconvex function H=H(φ,,k) is defined as a locally Lipschitz energy function. H is the generalized Clarke's gradient in the third variable k [6]. Assume that H fulfills the assumptions (A1), H(k)=min{h1(ν),h2(ν)}, and hi=RR(i=1,2) are convex quadratic functions [16].

    Now we take ˜A(φ)u()=B(φ)u() defined on H1(T,C). It is easy to see that A(φ)=A+˜A(φ) is a closed linear operator. Initially, we will show that A+˜A(φ) generates an evolution operator. It is well known that the solution of the scalar initial value problem

    p(φ)=n2p(φ)+q(φ),p(s)= 0, p(s)= p1,

    is given by

    p(t)=p1nsinn(φz)+1nφzsinn(φı)q(ı)dı.

    Therefore, the solution of the scalar initial value problem

    p(φ)=n2p(φ)+inB(φ)p(φ), (5.2)
    p(z)= 0, p(z)=p1, (5.3)

    satisfies the following equation:

    p(φ)=p1nsinn(φz)+iφzsinn(φı)B(ı)q(ı)dı.

    By the Gronwall-Bellman lemma, we obtain

    |p(t)|p1nec(φz) (5.4)

    for zφ and c is a constant. We denote by pn(φ,z) the solution of (5.2)-(5.3). We define

    Q(φ,z)u=n=1pn(φ,z)u,xnxn,uM,φR.

    It follows from the estimate (5.4) that Q(φ,z):MM is well defined and satisfies the condition of Definition 2.1. We set u(t)=u(φ,), that is, u(φ)()=u(φ,), φL, [0,π]. Then, we assume the infinite dimensional Hilbert space V, and we have

    V={v:vȷ=1vȷxȷ with ȷ=2v2ȷ<},

    with V as

    vV=(ȷ=2v2ȷ)12.

    Let us define BL(V,M) as below:

    Bv=2v2x1+ȷ=2vȷxȷ<,for all v=ȷ=2vȷxȷV.

    It continuous that

    Bv=(2v+v2)x2+ȷ=3vȷxȷ, for all v=ȷ=2vȷxȷM.

    Assume these functions satisfy the requirements of the hypotheses. From the above choices of the functions and evolution operator A(φ) with B, system (5.1) can be formulated as system (2.5) in M. Since all hypotheses of Theorem 4.4 are satisfied, the approximate controllability of system (5.1) on L follows from Theorem 4.4.

    The principles of approximate controllability of second-order differential impulsive systems with the impact of hemivariational inequalities are the main focus of this article. The generalized Clarke's subdifferential technique and multivalued maps were used to suggest and demonstrate the necessary requirements for existence and approximate controllability. In the future, we will extend the results with finite delay and stochastic systems.

    Yong-Ki Ma: Conceptualization, Methodology, Validation, Visualization, Writing–original draft. N. Valliammal: Conceptualization, Formal analysis, Methodology, Validation, Visualization, Writing–original draft. K. Jothimani: Conceptualization, Formal analysis, Investigation, Resources, Supervision, Writing–original draft. V. Vijayakumar: Conceptualization, Formal analysis, Resources, Supervision, Writing–original draft, Writing–Review & Editing. All authors have read and approved the final version of the manuscript for publication.

    This work was supported by the research grant of Kongju National University in 2024. The authors are immensely grateful to the anonymous referees for their careful reading of this paper and helpful comments, which have been very useful for improving the quality of this paper.

    The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.



    [1] G. Esposito, L. Frunzo, F. Liotta, A. Panico, F. Pirozzi, Bio-methane potential tests to measure the biogas production from the digestion and co-digestion of complex organic substrates, Open Environ. Eng. J., 5 (2012), 1–8. https://doi.org/10.2174/1874829501205010001 doi: 10.2174/1874829501205010001
    [2] G. Esposito, L. Frunzo, A. Giordano, F. Liotta, A. Panico, F. Pirozzi, Anaerobic co-digestion of organic wastes, Rev. Environ. Sci. Bio/Technol., 11 (2012), 325–341. https://doi.org/10.1007/s11157-012-9277-8 doi: 10.1007/s11157-012-9277-8
    [3] V. Luongo, M. R. Mattei, L. Frunzo, B. D'Acunto, K. Gupta, S. Chellam, et al., A transient biological fouling model for constant flux microfiltration, Math. Biosci. Eng., 20 (2023), 1274–1296. https://doi.org/10.3934/mbe.2023058 doi: 10.3934/mbe.2023058
    [4] Y. Li, S. Y. Park, J. Zhu, Solid-state anaerobic digestion for methane production from organic waste, Renewable Sustainable Energy Rev., 15 (2011), 821–826. https://doi.org/10.1016/j.rser.2010.07.042 doi: 10.1016/j.rser.2010.07.042
    [5] O. Karthikeyan, C. Visvanathan, Bio-energy recovery from high-solid organic substrates by dry anaerobic bio-conversion processes: A review, Rev. Environ. Sci. Bio/Technol., 12 (2013), 257–284. https://doi.org/10.1007/s11157-012-9304-9 doi: 10.1007/s11157-012-9304-9
    [6] P. Vandevivere, New and broader applications of anaerobic digestion, Critical Rev. Env. Sci. Technol., 29 (1999), 151–173. https://doi.org/10.1080/10643389991259191 doi: 10.1080/10643389991259191
    [7] G. Policastro, V. Luongo, L. Frunzo, N. Cogan, M. Fabbricino, A mechanistic mathematical model for photo fermentative hydrogen and polyhydroxybutyrate production, Math. Biosci. Eng., 20 (2023), 7407–7428. https://doi.org/10.3934/mbe.2023321 doi: 10.3934/mbe.2023321
    [8] A. Donoso-Bravo, C. Sadino-Riquelme, D. Gómez, C. Segura, E. Valdebenito, F. Hansen, Modelling of an anaerobic plug-flow reactor. process analysis and evaluation approaches with non-ideal mixing considerations, Bioresour. Technol., 260 (2018), 95–104. https://doi.org/10.1016/j.biortech.2018.03.082 doi: 10.1016/j.biortech.2018.03.082
    [9] I. Białobrzewski, K. Waszkielis, K. Bułkowska, The application of Anaerobic Digestion Model No. 1 for the optimization of biogas production from maize silage, pig manure, cattle manure, and digestate in a full-scale biogas plant, Fuel, 357 (2024), 129789. https://doi.org/10.1016/j.fuel.2023.129789 doi: 10.1016/j.fuel.2023.129789
    [10] R. Kothari, A. Pandey, S. Kumar, V. Tyagi, S. Tyagi, Different aspects of dry anaerobic digestion for bio-energy: An overview, Renewable Sustainable Energy Rev., 39 (2014), 174–195. https://doi.org/10.1016/j.rser.2014.07.011 doi: 10.1016/j.rser.2014.07.011
    [11] D. J. Batstone, J. Keller, I. Angelidaki, S. Kalyuzhnyi, S. Pavlostathis, A. Rozzi, et al., The iwa anaerobic digestion model no 1 (adm1), Water Sci. Technol., 45 (2002), 65–73. https://doi.org/10.2166/wst.2002.0292 doi: 10.2166/wst.2002.0292
    [12] C. De Crescenzo, A. Marzocchella, D. Karatza, S. Chianese, D. Musmarra, Autogenerative high-pressure anaerobic digestion modelling of volatile fatty acids: Effect of pressure variation and substrate composition on volumetric mass transfer coefficients, kinetic parameters, and process performance, Fuel, 358 (2024), 130144, https://doi.org/10.1016/j.fuel.2023.130144 doi: 10.1016/j.fuel.2023.130144
    [13] C. De Crescenzo, A. Marzocchella, D. Karatza, A. Molino, P. Ceron-Chafla, R. E. Lindeboom, et al., Modelling of autogenerative high-pressure anaerobic digestion in a batch reactor for the production of pressurised biogas, Biotechnol. Biofuels Bioprod., 15 (2022). https://doi.org/10.1186/s13068-022-02117-x
    [14] C. Fall, J. Loaiza-Navía, Design of a tracer test experience and dynamic calibration of the hydraulic model for a full-scale wastewater treatment plant by use of AQUASIM, Water Environ. Res., 79 (2007), 893–900. https://doi.org/10.2175/106143007X176068 doi: 10.2175/106143007X176068
    [15] Y. Muslu, Numerical approach to plug-flow activated sludge reactor kinetics, Comput. Biol. Med., 30, (2000), 207–223. https://doi.org/10.1016/S0010-4825(00)00009-3
    [16] V. Vavilin, L. Lokshina, X. Flotats, I. Angelidaki, Anaerobic digestion of solid material: Multidimensional modeling of continuous-flow reactor with non-uniform influent concentration distributions, Biotechnol. Bioeng., 97 (2007), 354–366. https://doi.org/10.1002/bit.21239 doi: 10.1002/bit.21239
    [17] B. Wu, Integration of mixing, heat transfer, and biochemical reaction kinetics in anaerobic methane fermentation, Biotechnol. Bioeng., 109 (2012), 2864–2874. https://doi.org/10.1002/bit.24551 doi: 10.1002/bit.24551
    [18] D. B. Panaro, M. R. Mattei, G. Esposito, J. P. Steyer, F. Capone, L. Frunzo, A modelling and simulation study of anaerobic digestion in plug-flow reactors, Commun. Nonlinear Sci. Numer. Simul., 105 (2022), 106062. https://doi.org/10.1016/j.cnsns.2021.106062 doi: 10.1016/j.cnsns.2021.106062
    [19] Y. Han, Z. Du, X. Hu, Y. Li, D. Cai, J. Fan, et al., Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM, Appl. Energy, 352 (2023), 122024. https://doi.org/10.1016/j.apenergy.2023.122024 doi: 10.1016/j.apenergy.2023.122024
    [20] A. Saltelli, K. Aleksankina, W. Becker, P. Fennell, F. Ferretti, N. Holst, et al., Why so many published sensitivity analyses are false: A systematic review of sensitivity analysis practices, Environ. Modell. Software, 114 (2019), 29–39. https://doi.org/10.1016/j.envsoft.2019.01.012 doi: 10.1016/j.envsoft.2019.01.012
    [21] A. Donoso-Bravo, J. Mailier, C. Martin, J. Rodríguez, C. A. Aceves-Lara, A. V. Wouwer, Model selection, identification and validation in anaerobic digestion: A review, Water Res., 45 (2011), 5347–5364. https://doi.org/10.1016/j.watres.2011.08.059 doi: 10.1016/j.watres.2011.08.059
    [22] B. Tartakovsky, S. Mu, Y. Zeng, S. Lou, S. Guiot, P. Wu, Anaerobic digestion model No. 1-based distributed parameter model of an anaerobic reactor: II. Model validation, Bioresour. Technol., 99 (2008), 3676–3684. https://doi.org/10.1016/j.biortech.2007.07.061 doi: 10.1016/j.biortech.2007.07.061
    [23] N. Noykova, M. Gyllenberg, Sensitivity analysis and parameter estimation in a model of anaerobic waste water treatment processes with substrate inhibition, Bioprocess. Eng., 23 (2000), 343–349. https://doi.org/10.1007/s004499900169 doi: 10.1007/s004499900169
    [24] O. Bernard, Z. Hadj-Sadok, D. Dochain, A. Genovesi, J. P. Steyer, Dynamical model development and parameter identification for an anaerobic wastewater treatment process, Biotechnol. Bioeng., 75 (2001), 424–438. https://doi.org/10.1002/bit.10036 doi: 10.1002/bit.10036
    [25] V. Vavilin, S. Rytov, S. Pavlostathis, J. Jokela, J. Rintala, A distributed model of solid waste anaerobic digestion: Sensitivity analysis, Water Sci. Technol., 48 (2003), 147–154. https://doi.org/10.2166/wst.2003.0241 doi: 10.2166/wst.2003.0241
    [26] Y. Lin, C. Wu, Sensitivity analysis of phenol degradation with sulfate reduction under anaerobic conditions, Environ. Model. Assess., 16 (2011), 213–225. https://doi.org/10.1007/s10666-010-9243-1 doi: 10.1007/s10666-010-9243-1
    [27] K. Solon, X. Flores-Alsina, K. V. Gernaey, U. Jeppsson, Effects of influent fractionation, kinetics, stoichiometry and mass transfer on CH4, H2 and CO2 production for (plant-wide) modeling of anaerobic digesters, Water Sci. Technol., 71 (2015), 870–877. https://doi.org/10.2166/wst.2015.029 doi: 10.2166/wst.2015.029
    [28] L. Benedetti, D. J. Batstone, B. De Baets, I. Nopens, P. A. Vanrolleghem, Global sensitivity analysis of biochemical, design and operational parameters of the Benchmark Simulation Model no. 2, in Proceedings of the 4th International Congress on Environmental Modelling and Software (iEMSs 2008), (2008), 1322–1330.
    [29] Ž. Zonta, M. Alves, X. Flotats, J. Palatsi, Modelling inhibitory effects of long chain fatty acids in the anaerobic digestion process, Water Res., 47 (2013), 1369–1380. https://doi.org/10.1016/j.watres.2012.12.007 doi: 10.1016/j.watres.2012.12.007
    [30] F. Carrera-Chapela, A. Donoso-Bravo, D. Jeison, I. D´ıaz, J. Gonzalez, G. Ruiz-Filippi, Development, identification and validation of a mathematical model of anaerobic digestion of sewage sludge focusing on H2S formation and transfer, Biochem. Eng. J., 112 (2016), 13–19. https://doi.org/10.1016/j.bej.2016.03.008 doi: 10.1016/j.bej.2016.03.008
    [31] I. M. Nasir, T. I. Mohd Ghazi, R. Omar, Anaerobic digestion technology in livestock manure treatment for biogas production: A review, Eng. Life Sci., 12 (2012), 258–269. https://doi.org/10.1002/elsc.201100150 doi: 10.1002/elsc.201100150
    [32] M. D. Morris, Factorial sampling plans for preliminary computational experiments, Technometrics, 33 (1991), 161–174. https://doi.org/10.1080/00401706.1991.10484804 doi: 10.1080/00401706.1991.10484804
    [33] M. Baudin, A. Dutfoy, B. Iooss, A. L. Popelin, Open TURNS: An industrial software for uncertainty quantification in simulation, preprint, arXiv: 1501.05242. https://doi.org/10.48550/arXiv.1501.05242
    [34] F. Campolongo, A. Saltelli, Sensitivity analysis of an environmental model: An application of different analysis methods, Reliab. Eng. Syst. Saf., 57 (1997), 49–69. https://doi.org/10.1016/S0951-8320(97)00021-5 doi: 10.1016/S0951-8320(97)00021-5
    [35] A. Trucchia, M. R. Mattei, V. Luongo, L. Frunzo, M. C. Rochoux, Surrogate-based uncertainty and sensitivity analysis for bacterial invasion in multi-species biofilm modeling, Commun. Nonlinear Sci. Numer. Simul., 73 (2019), 403–424. https://doi.org/10.1016/j.cnsns.2019.02.024 doi: 10.1016/j.cnsns.2019.02.024
    [36] A. Trucchia, V. Egorova, G. Pagnini, M. C. Rochoux, On the merits of sparse surrogates for global sensitivity analysis of multi-scale nonlinear problems: Application to turbulence and fire-spotting model in wildland fire simulators, Commun. Nonlinear Sci. Numer. Simul., 73 (2019), 120–145. https://doi.org/10.1016/j.cnsns.2019.02.002 doi: 10.1016/j.cnsns.2019.02.002
    [37] P. T. Roy, N. El Moçayd, S. Ricci, J. C. Jouhaud, N. Goutal, M. De Lozzo, et al., Comparison of polynomial chaos and Gaussian process surrogates for uncertainty quantification and correlation estimation of spatially distributed open-channel steady flows, Stochastic Environ. Res. Risk Assess., 32 (2018), 1723–1741. https://doi.org/10.1007/s00477-017-1470-4 doi: 10.1007/s00477-017-1470-4
    [38] D. Xiu, G. E. Karniadakis, The Wiener–Askey polynomial chaos for stochastic differential equations, SIAM J. Sci. Comput., 24 (2002), 619–644. https://doi.org/10.1137/S1064827501387826 doi: 10.1137/S1064827501387826
    [39] G. Blatman, B. Sudret, Efficient computation of global sensitivity indices using sparse polynomial chaos expansions, Reliab. Eng. Syst. Saf., 95 (2010), 1216–1229. https://doi.org/10.1016/j.ress.2010.06.015 doi: 10.1016/j.ress.2010.06.015
    [40] C. K. Williams, C. E. Rasmussen, Gaussian Processes for Machine Learning, MIT press Cambridge, MA, 2 (2006).
    [41] A. Trucchia, L. Frunzo, Surrogate based global sensitivity analysis of ADM1-based anaerobic digestion model, J. Environ. Manage., 282 (2021), 111456. https://doi.org/10.1016/j.jenvman.2020.111456 doi: 10.1016/j.jenvman.2020.111456
    [42] F. Charte, I. Romero, M. D. Pérez-Godoy, A. J. Rivera, E. Castro, Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass, Comput. Chem. Eng., 101 (2017), 23–30. https://doi.org/10.1016/j.compchemeng.2017.02.008 doi: 10.1016/j.compchemeng.2017.02.008
    [43] E. Ficara, S. Hassam, A. Allegrini, A. Leva, F. Malpei, G. Ferretti, Anaerobic digestion models: A comparative study, IFAC Proc. Vol., 45 (2012), 1052–1057. https://doi.org/10.3182/20120215-3-AT-3016.00186 doi: 10.3182/20120215-3-AT-3016.00186
    [44] D. Panaro, L. Frunzo, M. Mattei, V. Luongo, G. Esposito, Calibration, validation and sensitivity analysis of a surface-based ADM1 model, Ecol. Modell., 460 (2021), 109726.
    [45] C. Veluchamy, A. S. Kalamdhad, A mass diffusion model on the effect of moisture content for solid-state anaerobic digestion, J. Cleaner Prod., 162 (2017), 371–379 https://doi.org/10.1016/j.jclepro.2017.06.099 doi: 10.1016/j.jclepro.2017.06.099
    [46] F. Xu, Z. W. Wang, L. Tang, Y. Li, A mass diffusion-based interpretation of the effect of total solids content on solid-state anaerobic digestion of cellulosic biomass, Bioresour. Technol., 167 (2014), 178–185. https://doi.org/10.1016/j.biortech.2014.05.114 doi: 10.1016/j.biortech.2014.05.114
    [47] F. Liotta, P. Chatellier, G. Esposito, M. Fabbricino, E. D. Van Hullebusch, P. N. Lens, Hydrodynamic mathematical modelling of aerobic plug flow and nonideal flow reactors: A critical and historical review, Crit. Rev. Environ. Sci. Technol., 44 (2014), 2642–2673. https://doi.org/10.1080/10643389.2013.829768 doi: 10.1080/10643389.2013.829768
    [48] A. Marrel, B. Iooss, B. Laurent, O. Roustant, Calculations of Sobol indices for the Gaussian process metamodel, Reliab. Eng. Syst. Saf., 94 (2009), 742–751. https://doi.org/10.1016/j.ress.2008.07.008 doi: 10.1016/j.ress.2008.07.008
    [49] I. Sobolprime, Sensitivity analysis for nonlinear mathematical models, Math. Model. Comput. Exp., 1 (1993), 407–414.
    [50] A. Saltelli, M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, et al., Global Sensitivity Analysis: The Primer, John Wiley & Sons, 2008.
    [51] M. Baudin, K. Boumhaout, T. Delage, B. Iooss, J. M. Martinez, Numerical stability of Sobol'indices estimation formula, in Proceedings of the 8th International Conference on Sensitivity Analysis of Model Output (SAMO 2016), 30 (2016), 50–51.
    [52] J. Yang, L. Lu, W. Ouyang, Y. Gou, Y. Chen, H. Ma, et al., Estimation of kinetic parameters of an anaerobic digestion model using particle swarm optimization, Biochem. Eng. J., 120 (2017), 25–32. https://doi.org/10.1016/j.bej.2016.12.022 doi: 10.1016/j.bej.2016.12.022
    [53] N. Kythreotou, G. Florides, S. A. Tassou, A review of simple to scientific models for anaerobic digestion, Renewable Energy, 71 (2014), 701–714. https://doi.org/10.1016/j.renene.2014.05.055 doi: 10.1016/j.renene.2014.05.055
    [54] J. Y. X. Ling, Y. J. Chan, J. W. Chen, D. J. S. Chong, A. L. L. Tan, S. K. Arumugasamy, et al., Machine learning methods for the modelling and optimisation of biogas production from anaerobic digestion: A review, Environ. Sci. Pollut. Res., 31 (2024), 19085–19104. https://doi.org/10.1007/s11356-024-32435-6 doi: 10.1007/s11356-024-32435-6
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